Senior Data Engineer 1
About the job
- Deep understanding of data warehousing and dimensional modeling on cloud platforms, preferably Snowflake; familiarity with Data Vault is a nice to have.
- Deep understanding of system architecture for data workloads, including containers, orchestrators, schedulers, secrets, networking, and storage.
- Strong understanding of CI/CD, release management, and DataOps principles, including multi-environment promotion patterns and rollback strategies.
- Strong understanding of containerization patterns and Kubernetes for running data workloads, including deployments, jobs, cronjobs, resource limits, and autoscaling.
- Strong understanding of AWS cloud architecture patterns for data workloads, including VPC, IAM, S3, compute, networking, and security.
- Strong understanding of secrets management and least-privilege access patterns using Vault and/or AWS Secrets Manager.
- Strong understanding of data privacy, security, GDPR/PII, and access control (RBAC) on cloud data platforms.
- Experience in one or more relevant business domains, such as Finance, FP&A, Treasury, Procurement, or Market Intelligence, or transferable experience from comparable domains.
- Advanced Snowflake skills in performance tuning, clustering, RBAC, cost optimization, and governance.
- Advanced dbt skills in modular design, macros, testing, documentation, and project hygiene.
- Advanced Dagster (or Airflow or equivalent orchestrator) skills, including assets, partitions, sensors, IO managers, and resources.
- Strong object-oriented Python skills for orchestration, tooling, and data engineering.
- Strong Kubernetes skills for data workloads, including deployments, jobs, cronjobs, services, ingresses, resource sizing, and autoscaling; familiarity with Helm or Kustomize.
- Strong Docker skills in image design, multi-stage builds, layering, security, and registry workflows.
- Strong CI/CD experience on Harness or comparable enterprise CI/CD tools such as GitHub Actions, GitLab CI, or ArgoCD, including pipeline design, approval gates, secrets injection, and environment promotion.
- Strong hands-on AWS experience for data workloads, including S3, IAM, networking (VPC and security groups), Lambda, Glue, and CloudWatch.
- Strong Terraform skills in modules, state management, multi-environment patterns, and drift detection.
- Hands-on experience with secrets management using HashiCorp Vault and/or AWS Secrets Manager, including integration with Kubernetes and CI/CD.
- Strong Git workflows and code review discipline.
- Awareness of BI/dashboard concepts and semantic layers sufficient to collaborate with the Reporting team and design the data layer feeding them.
- Ability to own complex, cross-source pipelines and platform components end-to-end with minimal oversight.
- Ability to make pragmatic system architecture decisions for data workloads, choosing between Kubernetes, serverless, and managed services based on operational reality, cost, and team capability.
- Ability to make and clearly explain pragmatic trade-offs between speed, scalability, cost, and operability.
- Ability to manage stakeholders across the data team, brand teams, Risk & Control, and Infrastructure.
- Excellent English communication to engage technical and non-technical stakeholders.
- Proven ability to drive technical change and impact across multiple teams.
- Own critical data pipelines and the underlying platform end-to-end on Snowflake, dbt, Dagster, AWS, Kubernetes, and Terraform, including system design, implementation, performance, cost, reliability, and incident handling.
- Design the system architecture for data services and workloads on the team, including how containers, orchestrators, schedulers, secrets, and storage fit together, and evolve it as new use cases arise; partner with the Solutions Architect on cross-cutting decisions.
- Own Docker images for the team's data workloads and the Kubernetes deployments that run them; design manifests and Helm charts, and manage resource sizing, autoscaling, network policies, and storage classes for data pipelines on Kubernetes.
- Design and improve CI/CD pipelines on Harness for dbt projects, Dagster code, container images, and infrastructure repositories; harden deployment processes, manage multi-environment promotion (dev/test/prod), and codify safe rollback and approval gates.
- Own Terraform modules for the team's AWS footprint, including networking (VPC, subnets, and security groups), compute, IAM, storage, and supporting services; review infrastructure changes from mid-level engineers.
- Own secrets handling and access patterns for the team using HashiCorp Vault and AWS Secrets Manager, including least-privilege configurations, rotation policies, audit trails, and integration with Kubernetes and CI/CD.
- Apply Snowflake performance tuning, warehouse sizing, clustering, RBAC, cost optimization, and governance; partner with the Solutions Architect on platform-level decisions.
- Write high-quality, object-oriented Python for Dagster (assets, sensors, IO managers, and resources) and the team's operational tooling; set patterns and review code from mid-level engineers.
- Define and track SLIs/SLOs for data pipelines and platform components; implement proactive monitoring, alerting, and runbooks; lead incident root-cause analysis and post-mortems on the data side.
- Design and operate data flows in line with Risk & Control requirements, including auditability, lineage, access control, and segregation of duties, and support SOX-relevant reporting use cases.
- Review designs and pull requests for the team, mentor mid-level engineers on engineering and operational excellence, and help shape technical standards with the Solutions Architect.
- Partner with the Product Manager, Senior BAs, and engineering peers in brand teams to scope and prioritize work, and represent the team in technical discussions with stakeholders across supported business areas.
- Familiarity with Data Vault.
- Working knowledge of Risk & Control/SOX requirements for financial and regulatory data and reporting.
- Work experience with Streamlit or similar lightweight data app frameworks.
- Hands-on experience using AI/LLM-based tools to accelerate engineering work, including code generation, code review, debugging, documentation, and data exploration.
About the company
Booking.com is an online travel marketplace that connects travelers with a wide range of travel services, including hotels, apartments, flights, and car rentals. Users search for destinations, compare options, read reviews, and book accommodations or other travel services all in one place through a single, user-friendly interface. The platform makes money by taking a commission on each booking from properties (typically 15%–25%) and by selling advertising space so listings can gain more visibility. What sets Booking.com apart is its large global inventory and centralized experience, making it easy for travelers to plan and complete trips in one place while giving partners access to a broad customer base. The company’s goal is to simplify travel planning at a global scale by offering a comprehensive, convenient platform that helps both travelers and travel providers connect efficiently and reliably.
Company Size
10,001+
Company Stage
Acquired
Total Funding
N/A
Headquarters
Amsterdam, Netherlands
Founded
1996
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Simplify's Take
What believers are saying
- Air India integrated Booking.com inventory on June 22, 2026, widening distribution.
- Bengaluru lease expansion on September 12, 2026 signals sustained India engineering investment.
- Lola’s launch on September 29, 2026 positions Booking.com inside AI-driven travel commerce.
What critics are saying
- Which? exposed fake London listings on September 2 and September 25, 2026.
- Ofcom investigations and Online Safety Act enforcement threaten fines, takedowns, and stricter verification.
- General-purpose AI agents bypass Booking.com’s gatekeeper role, crushing referral traffic and commissions by 2027.
What makes Booking.com unique
- Booking.com combines 31 million listings with flights, cars, restaurants, and experiences.
- Lola launched September 29, 2026, bundling travel planning and booking inside one conversation.
- Amsterdam court upheld key elements of Booking.com’s price-parity position on September 14, 2026.
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Benefits
Paid Vacation
Flexible Work Hours
Hybrid Work Options
Remote Work Options
Employee Discounts
Parental Leave
Bereavement Leave
Growth & Insights and Company News
6 month growth
0%1 year growth
0%2 year growth
0%GT Ferreira backs LekkeSlaap as founders sell up. LekkeSlaap co-founders Jonathan Womersley and Marcel van de Ghinste have sold the popular accommodation booking platform. LekkeSlaap, the South African accommodation booking platform and rival to Airbnb, has been sold to a consortium of local investors that includes the Dippenaar and Ferreira family offices and Investec. Co-founders Jonathan Womersley and Marcel van de Ghinste have exited the business. The transaction value was not disclosed. The consortium is co-led by Bellair Road Investment Partners and Janic Capital, the Dippenaar family's investment vehicle, led by Adriaan Dippenaar. It also includes the Ferreira Family Office - the family office of Rand Merchant Bank and FirstRand co-founder GT Ferreira, LekkeSlaap confirmed to TechCentral - the Moolman Family Office and Investec. LekkeSlaap's executive management team, led by CEO Frans Joubert, has invested alongside the consortium and will continue to run the business. The deal marks the exit of Womersley, Van de Ghinste and other longstanding shareholders from a business that began as a garage-based travel tech start-up in 2009. LekkeSlaap itself launched in 2013 and now lists more than 40 000 properties across Southern Africa. It says it has served more than nine million guests since it was founded. The deal covers all of Tripco, LekkeSlaap's parent company, of which LekkeSlaap is the main consumer-facing brand, LekkeSlaap told TechCentral. "To see LekkeSlaap grow from a small start-up into a brand used and loved by millions of South Africans has been incredibly rewarding," said Womersley in a statement on Thursday. "After so many years, finding new owners who share our culture, values and vision for the business matters enormously to us." The new owners have brought in two well-known South African business leaders as directors, both of whom have also invested. New board members. Herman Bosman, chairman of the Outsurance Group and Fortress Real Estate and a former CEO of RMB Holdings and RMI Holdings, has been appointed chairman of LekkeSlaap. He has invested through Atelier Partners. He is joined on the board by Rob Paddock, co-founder of online education company GetSmarter, which was sold to US-listed 2U for about R1.4-billion in 2017, and of Valenture Institute. "LekkeSlaap is a great example of what South African entrepreneurs can build when they combine a strong customer proposition with persistence and ambition," said Bosman in Thursday's statement. Bellair Road Investment Partners focuses on long-term growth investments, and its portfolio includes Montego Pet Nutrition, Econofoods and Freedom of Movement. LekkeSlaap competes with global platforms including Booking.com and Airbnb for South African travellers. Joubert said the company would use the new backing to expand further across Southern Africa and invest more in technology and localisation. "LekkeSlaap remains proudly local and independent in spirit, but with the right partners behind us, we can now dream even bigger," said Joubert. - (c) 2026 NewsCentral Media
How Booking.com power-packs GenAI at scale. October 8, 2026 Browsing properties or booking a stay on Booking.com triggers a massive real-time machinery of over 480 machine learning models running simultaneously behind the scenes. Operating at a scale that processes anywhere from 500 billion to 800 billion predictions per day with 99.99% reliability, maintaining sub-20-millisecond latency while integrating non-deterministic Large Language Models (LLMs) into production presents a unique engineering challenge. At the 2025 edition of the Nordic Data Science and Machine Learning (NDSML) Summit, Sanchit Juneja, Product Lead for Big Data and Machine Learning at Booking.com, shared more about "LLMOps: Factory Floor for GenAI in Booking.com". His session detailed how the travel giant evolved an industry-leading MLOps ecosystem into a specialized LLMOps architecture designed to handle generative AI at enterprise scale. ROI over research. Before diving into hardware and pipelines, Juneja established a clear distinction between academic AI research and applied enterprise engineering. While research institutions focus on foundational model training, for-profit organizations focus on operational execution: FM Ops (Foundation Model Operations) and its subset, LLM Ops. Just as software engineering created DevOps and security yielded SecOps, the complexity of managing live generative models requires a dedicated tooling layer: the "factory floor" that turns AI research into measurable business revenue. From MLOps to LLMOps. In traditional machine learning, production systems rely heavily on structured data, self-hosted models, and deterministic outputs. As Juneja explained during the presentation, the rise of LLMs forced a fundamental architectural shift across enterprise environments: * Unstructured Data Ingestion Production stacks must now seamlessly process direct user text, audio, and image uploads rather than relying strictly on formatted protocol buffers. Advertisement - [email protected] * LLM-as-a-Service & Middleware Wrappers Not every business problem requires self-hosted models. Third-party hosted models can be leveraged through customized middleware layers. They can provide domain-agnostic access across external vendors without compromising compliance or performance. * Deterministic Guardrails Serving travelers urgent updates requires speed and precision. Implementing specialized Prompt Stores, Vector Stores, and Graph Neural Networks enforces determinism and low latency over non-deterministic base models. * Hardware Evolution Scaled GenAI requires distributed GPU inference alongside specialized hardware accelerators like XPUs and Groq to maintain real-time performance. Governance, feedback loops, and the rise of slms. Deploying models at global scale requires rigorous oversight. Compliance with evolving governance frameworks (such as the EU AI Act) demands continuous monitoring for model drift, toxicity, and algorithmic bias. To maintain quality, Booking.com leverages automated feedback loops. It incorporates Reinforcement Learning from Human Feedback (RLHF) to continuously retrain stale models and streamline label collection. Looking toward the future, Juneja highlighted a shift away from ever-larger foundational models toward Small Language Models (SLMs). Hyper-specialized 1B to 3B parameter models offer light compute footprints, making them ideal for targeted enterprise tasks and edge deployments. Go deep into the production architecture. From evaluating models using "LLM-as-a-Judge" frameworks to fine-tuning targeted Small Language Models (SLMs) and managing RLHF feedback loops, the presentation provides the operational plan needed to move from generative hype to high-ROI production systems. This overview barely scratches the surface of the full operational playbook. In the complete session recording, Sanchit Juneja breaks down the exact schematic of Booking.com's model serving layer, data lake orchestration, guardrailing strategies, and GPU cost-optimization tools like Run:ai. Access the full video to explore the exact technical details and see how top-tier engineering teams construct production-ready LLM ecosystems. Join the next NDSML Summit. With capacity strictly capped at 400 delegates, and over 70% of onsite places already booked, enterprise teams are now finalizing their plans for this year's edition of the NDSML Event. Discover cutting-edge strategies directly from world-class data scientists, MLOps architects, and AI pioneers in person. Reserve a spot for the upcoming NDSML Summit in Stockholm to participate in technical tracks, executive panels, and networking sessions with leaders driving the future of enterprise AI. Published October 08, 2026 October 7, 2026 October 6, 2026 October 5, 2026
Booking Holdings spends nearly $9 billion a year to reach travelers. Its CFO explains how AI is changing trip planning. 1 hour ago 38 Good morning. The biggest impact of AI on travel may happen before the traveler ever checks into a hotel or boards a plane. At the Fortune AIQ Summit on Oct. 1, I interviewed Ewout Steenbergen, EVP and CFO of Booking Holdings (No. 169 on the Fortune 500), which owns and operates several major online travel brands, including Booking.com, Priceline, Agoda, KAYAK, OpenTable, and Rentalcars.com. Steenbergen described a future in which AI makes travel planning more seamless, personalized, and proactive. That could mean suggesting an indoor visit to the Louvre when rain threatens a Paris itinerary, automatically moving a restaurant reservation after a delayed flight, or recommending an activity based on a traveler's preferences and open time. The prize is not simply a better chatbot. It is what Booking calls a "connected trip": an integrated experience that ties together flights, hotels, restaurants, rental cars, and activities. And, in doing so, potentially creates more loyalty, trust, and frequency of engagement. From AI pilots to unit economics. For finance leaders, Steenbergen's comments offered a practical framework for judging AI investments. His message: Move past isolated pilots, but keep a close eye on unit economics. Booking spends roughly $8 billion to $9 billion annually on paid customer-acquisition channels, including search, social, and metasearch platforms, Steenbergen said. About one-third of its consumer traffic comes through paid channels, while the other two-thirds comes directly to its platforms. As consumers turn to more AI tools and agents for discovery, he sees greater channel diversification as potentially positive for Booking's unit economics. Early signals are encouraging. Customers using Booking's AI tools are booking somewhat faster, converting at somewhat higher rates, and canceling less often, Steenbergen said, while cautioning that the data remains early stage. I asked him how he's measuring the financial return on investment when it comes to AI. "I think in this area there isn't a perfect answer," he said. He continued, "I even think that the hyperscalers that spend hundreds of billions on the development of their large language models, they don't really know what is going to be the ROI," Steenbergen said. "They have maybe some assumptions and some hypotheses, but the whole point is, you don't want to fall behind." He argued that the biggest returns come not from layering AI onto existing processes, but from rebuilding processes end to end. In customer service, Booking has seen booking units grow at a high single-digit rate while total customer-service costs declined slightly, he said. Customer satisfaction increased at the same time, driving down average servicing cost per booking. In engineering, the company's roughly 9,000 technologists have used AI tools to increase the number of merge requests put into production by about 30%, Steenbergen said. Crucially, that figure reflects code that has been tested and cleared quality controls - not merely generated. The CFO's job, then, is to measure both sides of the ledger. Steenbergen said Booking monitors token and licensing costs and uses "effective model-cost routing," reserving lower-cost or open-source models for simpler tasks and more expensive models for complex ones. One engineering metric combines human and AI costs: total IT cost per merge request. That is the more mature AI-finance question. Not whether a model costs more in isolation, but whether it lowers the all-in cost of producing a valuable outcome. Steenbergen is applying the technology personally, too. He has an "AI coach" and uses two AI agents: a "strategic thought partner" for board materials and strategic analysis, and a "critical equity research agent" to pressure-test earnings-call preparation. Watch the full video of my interview with Steenbergen at the Fortune AIQ Summit here. Leaderboard. Fortune 500 Power Moves Cody Perdue was named interim CFO at Albertsons Companies (No. 57). Perdue succeeds Sharon McCollam, who previously announced plans to retire as president and CFO and will remain with the company in an advisory capacity through the end of the fiscal year on Feb. 27, 2027, to support the transition. Albertsons has begun a search for a permanent CFO. Perdue, who joined Albertsons in 2013, currently serves as SVP of treasury, investor relations, and risk management and will continue to oversee those functions in his interim role. He has also held positions including group VP of treasury and investor relations, as well as finance and accounting leadership roles. Before joining Albertsons, Perdue worked at Deloitte & Touche LLP. The weekly Fortune 500 Power Moves column tracks Fortune 500 company C-suite shifts - see the most recent edition. More notable moves Raju Datla was appointed CFO of MobileX, a wireless company. Datla joins from RedCloud Holdings, where he served as CFO. His prior roles include chief strategy officer at Tillman Fiber and CFO and co-founder of NxtGen Datacenter & Cloud Technologies. Earlier, he worked in investment banking at Deutsche Bank, advising technology, media, and telecommunications clients. Angela Sweeney was appointed CFO of Zircon Corporation, a Silicon Valley-based maker of sensor-based electronic hand tools. Sweeney joins Zircon with more than 20 years of finance and operations experience across high-growth manufacturing and technology companies. Her background includes senior finance and operating roles at Poplar Homes, Astra, Engagor, and Venga Global, as well as advisory work with founders and executive teams. Big deal. Cybersecurity is becoming a more consequential capital-allocation issue for CFOs as companies expand their use of AI. PwC's 2027 Global Digital Trust Insights survey of 3,934 business and technology executives found that 84% of security and finance leaders expect cyber budgets to increase, up six percentage points from last year, with AI a top investment priority. Yet half of security leaders say attacks targeting AI systems are among the threats their organizations are least prepared to address - ranking ahead of cloud-related threats, third-party breaches, and ransomware. The report also points to foundational resilience gaps: Companies, on average, have implemented only three of seven surveyed data-risk measures, and just 39% have fully formalized operational-continuity plans that specifically address cyber threats. For finance leaders, realizing AI's value will require funding the data protections, controls, talent, and continuity planning needed to contain financial and operational risk. Going deeper. CFOs pursuing AI-led growth may face a tightening market for the talent needed to deliver it. CompTIA's September analysis found that U.S. employers posted 272,040 new technology job openings, bringing active postings to 625,633 - the highest level in more than three years and 22.4% above September 2025. Demand for AI capabilities is especially pronounced: active job postings seeking AI skills rose by 32,327 from August to 349,821, up roughly 282% since May 2024. The figures suggest finance leaders will need to weigh the cost of recruiting specialized talent against reskilling existing teams, outsourcing work, and redesigning processes around AI. Overheard. "There is nothing more valuable in hiring than a personal recommendation or a referral. It should be a reminder that growing and maintaining your professional network is a key part of your career. You need to be a person others would vouch for." - Paddy Lambros, founder and CEO of AI recruiter Dex, writes in a Fortune opinion piece. * Homepage * Fortune Magazine * Booking Holdings spends nearly $9 billion a year to reach travelers. Its CFO explains how AI is changing trip planning.
Booking.com just launched Lola, an AI Agent that can plan and book your entire trip. Sep 29, 2026 Booking.com has launched Lola, a dedicated AI travel agent that can search real-time availability, compare prices, and book hotels, restaurants, events, and entire trips in a single conversation across Booking Holdings brands like Kayak, Priceline, and OpenTable plus partners like SeatGeek and GetYourGuide, as travel companies push back against general-purpose agents like Meta's Muse that threatened to bypass them. The new "it" thing to do is create an AI Agent for your brand, and Booking.com is the latest to do just that. Today, Booking.com announced its new AI Agent, Lola, which will make it easier for you to book your travel. The company says that it can offer "personalized recommendations, live availability and pricing, direct booking, preferred rates and benefits." Lola can also recommend and reserve hotels, restaurants, events, activities, or entire trips. Launch partners include ResX, BLADE, Aero, SeatGeek, GetYourGuide, Nuitee, and Ten Group, as well as Booking's own brands. This includes Booking.com, OpenTable, Kayak, Priceline, Agoda, and FareHarbor. The app can search for real-time availability and pricing, letting users compare options and book within the same conversation. Lola can understand natural language, ask follow-up questions, and learn about users to make recommendations more personalized. Lola is one of the first dedicated AI agents for booking travel. It looks like Booking.com's Lola is one of the first dedicated AI Agents for bookings and travel. While Muse was the first to arrive with similar capabilities, it is part of Meta and can do much more than just bookings and travel. However, when Meta's Muse first launched, it caused travel-booking stocks to fall amid fears that the AI Agent could bypass dedicated travel sites altogether. But it looks like the dedicated travel sites are fighting back. As always with AI, it's important to double-check things and still make the final booking yourself, as AI can hallucinate. Most AI agents will put together an itinerary for you and then give it to you for final approval before booking. Agents like Muse also take it a step further by asking for your info, so you can just enter your credit card and book it yourself. Making things so much easier. This is where AI Agents will really show their worth.
KAYAK co-founders launch AI assistant Lola for travel experiences. * Sixuan Wu * September 29, 2026 [Credit: Lola] Reading Time: 2 minutes US: KAYAK co-founders Steve Hafner and Paul English have launched Lola, an AI assistant for travel, dining and experiences backed by Booking Holdings. Lola provides personalised recommendations for trips, restaurants and events with real-time availability and pricing, allowing users to compare options and book directly through the same conversation. Anyone can become a member of Lola for free, with paid membership plans providing preferred hotel rates, priority dining, concierge access, and more. Pricing includes The Plus membership at $5 per month and the VIP at $25 per month. Lola's offerings include: * Hotels - all membership plans include results from multiple travel sites, with paid plans offering preferred discounted rates * Dining - paid plans provide advance access to restaurant reservations * Events - members receive access and preferred pricing on more than 200,000 experiences worldwide, including sporting events, concerts, museums and attractions * Paid members also receive discounts for airport and concierge services Launch partners include ResX, BLADE, Aero, SeatGeek, GetYourGuide, Nuitee and Ten Group, alongside Booking Holdings brands including Booking.com, OpenTable, KAYAK, Priceline, Agoda, and FareHarbor. "We built Lola to give everyone the advantage of being well connected," Hafner said. "The next generation of AI assistants should do more than answer questions. They should understand what you're looking for, help you decide, and make it happen." English added: "Lola learns what each member likes and searches across hotels, restaurants, events, and experiences to find the options worth their time, with member rates and perks attached. We're building Lola to be comprehensive, and there are many more partners to come." Highlights: * KAYAK co-founders Steve Hafner and Paul English have launched Lola, an AI assistant for travel, dining and experiences, backed by Booking Holdings. * Lola provides personalised recommendations with real-time availability and pricing, allowing users to compare options and book hotels, restaurants and experiences directly within the same conversation. * Lola offers free and paid memberships, with Plus costing $5 per month and VIP costing $25 per month. Paid members receive benefits including preferred hotel rates, priority restaurant reservations and concierge access. * The platform covers more than 200,000 experiences worldwide, including sporting events, concerts, museums and attractions, with members receiving access and preferred pricing. * Lola has partnered with companies including Booking.com, OpenTable, KAYAK, Priceline, Agoda, SeatGeek, GetYourGuide and Ten Group, and is headquartered in New York City.