Business Development Representative - Commercial
About the job
- Experience prospecting into enterprise or commercial customers, identifying key decision makers, understanding customer challenges, and securing sales-qualified opportunities.
- Experience pitching to vice-president-level and individual-contributor-level audiences.
- Experience with cold calling.
- Written and verbal communication skills, including proper grammar.
- High-level understanding of the enterprise or commercial data space.
- Map accounts within large enterprise or commercial companies, prospect and generate meetings with employees at all levels, from C-level executives to data analysts.
- Show a deep interest in commercial customers’ data challenges and pain points, explain Fivetran’s offering at the enterprise and commercial levels, and set up valuable engagements with account executives.
- Collaborate with commercial account executives, Marketing, and Alliances to generate pipeline and exceed revenue goals.
- Follow predesigned sales strategies and, when appropriate, develop and implement strategies to grow the business and meet account acquisition targets.
- Use the Fivetran consultative, Command of the Message, and Challenger sales processes; use pre-created cadences based on industry, persona, and use case, with personalization.
- Create personalized cadences for specific accounts and use cases, and follow team plays for Snowflake Customer, SAP, or HVR use cases.
- Organize, log activity, and categorize sales lead information in Salesforce.com.
- At least six months of previous SDR or BDR experience.
About the company
Fivetran provides an automated data movement platform that handles ELT (extract, load, transform) to help organizations move and organize data from many sources to analytics destinations. It uses over 400 pre-built, no-code connectors to connect sources to targets, automatically managing data updates, normalization, and schema drift, so data is ready for analysis with minimal manual work. The platform emphasizes security with configurable, compliant deployments to meet GDPR, HIPAA, and other protections. Compared with peers, Fivetran stands out by offering a large library of ready-made connectors and end-to-end automation that reduces the time data engineers spend on building and maintaining data pipelines. The goal is to simplify and automate data integration for businesses of all sizes, enabling reliable, secure data pipelines and letting data teams focus on higher-value tasks.
Company Size
1,001-5,000
Company Stage
Debt Financing
Total Funding
$852.7M
Headquarters
Oakland, California
Founded
2012
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Simplify's Take
What believers are saying
- September 16, 2026 launched dbt v2, dbt State, and Fivetran Context Layer.
- Oakbrook Finance chose Fivetran on June 25, 2026, saving three months of build time.
- April 14, 2026 London office expansion supports more than 45 hires.
What critics are saying
- June 2026 merger integration risks product focus, pricing, and sales execution through 2027.
- Fivetran's benchmark accuses APIs and egress fees; AWS, Salesforce, and Snowflake can retaliate.
- If Snowflake, Databricks, and Salesforce tighten APIs, Fivetran's connector moat erodes fast.
What makes Fivetran unique
- June 2026 merger with dbt Labs bundles ingestion, transformation, and governed analytics in one platform.
- Fivetran's connectors auto-handle schema drift and API changes across 400-plus sources.
- Managed Data Lake Service keeps customer data in owned cloud storage on Apache Iceberg.
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Benefits
Health Insurance
Unlimited Paid Time Off
Stock Options
Professional Development Budget
Phone/Internet Stipend
Mental Health Support
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 0%2 year growth
↑ 0%dbt v2 goes GA: what the Rust rewrite and dbt State mean for your data team. At dbt Summit 2026 on September 16, Fivetran + dbt Labs made dbt v2, a full Rust rewrite, generally available alongside dbt State and a wave of agent-focused previews. By eslam elhadedy
Fivetran + dbt Labs announces new capabilities to make enterprise data agent-ready at dbt summit 2026. Last edited on Sep 16, 2026 Fivetran + dbt Labs makes dbt v2 and dbt State generally available, alongside the debut of Fivetran Context Layer, dbt Charts and a new open lakehouse vision for greater flexibility across storage and compute LAS VEGAS - September 16, 2026 - Fivetran + dbt Labs today announced the general availability of dbt v2 and dbt State, delivering new levels of speed and cost optimization, and introduced Fivetran Context Layer, new dbt Wizard experiences, dbt Charts and an open lakehouse vision for greater flexibility across storage and compute. As enterprises deploy AI agents into production, they need trusted data, context the business controls, and the flexibility to work across the platforms, models and tools already in use. To meet these demands, Fivetran + dbt Labs is advancing its vision for Open Data Infrastructure: a vendor-neutral, interoperable architecture that lets organizations independently choose and evolve their storage, compute, data movement, transformation and visualization technologies at every layer. This enables AI systems to work across platforms using data and context the enterprise owns, not a single vendor. "Every model our customers have built, every test they've written, every metric they've defined already captures the context AI agents need to do meaningful work," said Anjan Kundavaram, Chief Product Officer, Fivetran + dbt Labs. "What we're delivering now is the open infrastructure to put that context to work across systems, while giving organizations the freedom to choose how their data is stored, moved, transformed and used as AI evolves." A faster, more efficient engine with deeper SQL understanding dbt v2 is a full Rust rewrite of the dbt engine built for the scale that teams run at today and for how agents write SQL. It parses a 10,000-model project up to 10x faster than v1 and gives teams and their agents accurate real-time feedback, surfacing errors, column checks, and lineage before anything runs. With this release, the two engine era of Core and Fusion ends. Now, dbt is one engine with two versions: dbt Core v1, the python implementation, is dbt v1. Fusion, the Rust implementation, has become dbt v2. Both versions remain Apache 2.0-licensed and security-supported. dbt State determines what has changed by checking warehouse metadata and model SQL, then builds, skips, clones or defers each run accordingly. This simplifies orchestration and allows engineers to iterate faster without complex development rituals, while reducing unnecessary warehouse compute. "dbt State has been a paradigm shift for how we work," said Gordon Curzon, Head of Analytics Engineering, Virgin Media O2. "With freshness codified, simpler orchestration, and freed-up developer capacity, we focus more time on initiatives that add value to our business on top of the 25% savings on both job run time and BigQuery compute costs." More flexibility across storage and compute Open Data Infrastructure centers on a customer-owned data layer built on open formats, avoiding vendor lock-in for storage and compute so organizations can store data once and access it through different engines for different use cases. Fivetran's Managed Data Lake Service, already generally available, organizes, structures and maintains data as managed Apache Iceberg(TM) tables in customers' own cloud storage. Lake Compute, now in Private Beta, is a single-node SQL engine built on DuckDB and runs dbt models directly against Apache Iceberg(TM) tables, built and priced specifically for transformation, not general-purpose compute. Together, the two give teams the flexibility to run each workload on whichever engine fits best, optimizing cost without re-platforming. An open standard for agent context Agents are only as trustworthy as the context they can access. Fivetran Context Layer (Private Beta) unifies the data and metadata needed to give LLMs and AI agents relevant context, building on dbt's structured context and adding unstructured knowledge, like docs and Slack threads. This service uses Agents Schema, an open source standard, that centralizes context in a structured, extensible format directly in the data warehouse. The context is accessible to teams via preferred MCP or AI tools, including generally available integrations through AI marketplaces including Anthropic and a plugin in ChatGPT. One agent, grounded in your dbt project - wherever you work Coding agents can now write SQL as well as most engineers, but writing code isn't the same as understanding a governed dbt project, including its lineage, its tests, its contracts, and what breaks when something changes. dbt Wizard in the dbt platform (Public Preview) is built to close that gap. It's natively connected to your project, knows which tool to call, pulls the right context automatically, and proactively validates changes before they ship. Wizard is also expanding beyond the dbt platform with Wizard CLI (Public Beta), bringing the project-grounded agent directly into the terminal, and Wizard Desktop (Private Beta), a dedicated local workspace for longer, more complex work. Wizard Explore Mode (Public Preview) brings conversational analytics to business users, enabling them to ask questions in plain language and get answers grounded in the same dbt project the data team maintains. When an answer falls short, those questions can also surface what the data team should improve next. A shared language for BI, built for humans and agents dbt Charts (Public Beta) brings governed BI alongside the models it depends on. Instead of governance living in a separate, closed tool, they are defined as YAML and version-controlled alongside the dbt models they reference, creating a shared, declarative format that both humans and AI agents can read, write and review. Customers building with Fivetran + dbt Labs "Since rolling out dbt State, we've reduced warehouse costs by 59% on scheduled jobs in dbt platform," said Chris Shepherd, Principal Data Engineer, RxBenefits. "That's $8,173.23 in the first 60 days alone on top of a Snowflake adaptive warehouse. We've reused 716k models instead of rebuilding, which reduced query run time a total of 14 days, 11 hours, and 15 minutes over the same period." "We've been impressed by the flexibility Lake Compute gives us. Now we can choose where each dbt workload runs, and use whichever engine actually fits the job," said Tyson Doberneck, Senior Data Engineer, Obie. "dbt Wizard is changing how we work. Instead of hand-coding everything, we draft logic with an agent that already has full context on our jobs and our codebase. No more finding and uploading a manifest file just to explain myself, that step used to slow down every request. Now we're pointing it at sales and marketing data too, so analysts get answers themselves instead of waiting on my team," said Farin Fukunaga, Data Engineering Lead, Paylocity. About Fivetran + dbt Labs Fivetran + dbt Labs deliver the data infrastructure layer that makes agents trustworthy - from the moment data moves, through every transformation, to the context an agent reasons from. The Fivetran platform moves, manages, and transforms data from every system a business runs on into a secure, reliable foundation engineered to evolve, with the flexibility to work across clouds, engines, and tools. With Fivetran, analytics, operations, and AI run on data you trust and control. Thousands of organizations worldwide, including OpenAI, LVMH, Pfizer, and Verizon, rely on Fivetran to turn data into a competitive advantage. Learn more at Fivetran.com, or follow Fivetran on LinkedIn. Since 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Globally, more than 100,000 data teams use dbt, including those at Siemens, Roche and Condé Nast. Learn more at getdbt.com, and follow dbt Labs on LinkedIn, X, Instagram, and YouTube.
Fivetran promotes Natasha Lockwood to Lead of Product-Led Growth Marketing. Fivetran has promoted Natasha Lockwood to Lead of Product-Led Growth Marketing, expanding her responsibilities within the company's marketing organization. Lockwood's role focuses on product-led growth (PLG), an approach that connects product experiences with customer acquisition, engagement, and expansion. Her appointment comes as B2B software companies continue to explore ways to make the product itself a larger part of the marketing and customer journey. At Fivetran, Lockwood has built experience in digital strategy and cross-channel marketing. Her professional profile describes her focus as PLG marketing within B2B SaaS, alongside expertise in digital strategy and cross-channel campaigns. The expanded role places product-led growth more directly within Fivetran's marketing efforts. For B2B technology companies, PLG can involve using product usage signals, customer behaviour, digital experiences, and targeted campaigns to create more connected paths from initial interest to adoption. The shift is also part of a broader change in B2B marketing, where traditional lead-generation models are increasingly being complemented by product experiences. Rather than relying only on sales outreach or content to move prospects through the funnel, PLG strategies can use how customers interact with a product to inform marketing and engagement. For marketers, this creates a closer relationship between marketing, product, data, and customer experience teams. It also places greater importance on understanding user behaviour and identifying opportunities to support adoption throughout the customer lifecycle. Lockwood's promotion reflects that evolving role of marketing within SaaS businesses, where product engagement and customer behaviour are becoming increasingly important inputs for growth strategies. Her new position at Fivetran will focus specifically on product-led growth marketing as the company continues to develop its approach to reaching and engaging B2B technology buyers.
4 trends for activating data with context. Takeaways from Slalom's Insurance Leaders Lunch at the Databricks Data & AI Summit. Matt Edwards Senior Director Published: Aug 17, 2026 Tl;dr. * Slalom, in partnership with Atlan, Fivetran and Databricks, hosted an Insurance Leaders Lunch and panel discussion during the 2026 Databricks Data & AI Summit. Insurance, data, and AI leaders discussed, How do insurers move from having data to activating it to driving smarter decision-making? * The session opened by framing the challenges around four interconnected trends reshaping the industry. Those trends are human-AI collaboration, data fluidity and intelligent systems, ecosystem modularization, and self-directed systems. * The throughline connecting all four trends? Context. Context = shared definitions, data lineage, quality, regulatory constraints, and the "why" behind a recommendation. - Without context, even the most sophisticated AI systems fall flat in insurance operations. Thank you to its community of panelists: * Tom Linton, Head of World Wide Sales Engineering, Atlan * Marcela Granados Lavoie, Principal, Global Head of Insurance, Databricks * Zach Taher Vice President, Data Engineering, Northwestern Mutual * Dylan Austin, Vice President, MLOps Engineering, PURE Insurance What one foundational capability must insurers get right in the next two years? The panel's answer was remarkably aligned: All panelists agreed that data quality and governance, paired with intentional information architecture is the most important foundational capability for insurance industry technology and LOB leaders. You cannot build trustworthy AI on untrustworthy data. Invest in the foundations you need for clean, well-defined, well-governed data with rich context attached. What are the larger set of challenges reshaping the insurance industry right now? Trend 1: Human+AI collaboration. Two ideas came forward around human+AI collaboration: AI for augmentation, and trust through transparency. AI for augmentation The panel was clear: underwriters, claims professionals, and agents aren't replaceable. They're essential. At the same time, insurance professionals' impact can - and must - be augmented with AI. AI makes it possible for insurance pros to complete their goals, deliver work that's more consistent, and make better informed decisions. Trust through transparency The discussion highlighted how technology platforms can surface data lineage and business definitions directly within AI-powered workflows. Practitioners can examine the "why" behind a recommendation, not just see and nod along to the output. When end users actively shape AI, trust compounds over time. Trend 2. Data fluidity and intelligent systems. The insurance industry has plenty of data. Data shortage hasn't been a problem in a long time. The problem is data accessibility and trust problem. That problem becomes more challenging with the evolution to more modular ecosystems. Data that is discoverable, well-governed, and that can flow to the right system - at the right time - with the right context attached - is critical for developing insights and driving transformation. Modern platforms like Databricks and Atlan are enabling insurers to connect disparate data sources with the appropriate context to build richer, AI-ready data products. Insurance organizations are still working to address siloed systems that continue to create blind spots that directly impact underwriting accuracy, claims outcomes, and fraud detection. What's the practical starting point? The panel agreed that leaders must invest in discoverability and metadata management before trying to build intelligent systems on top of data you can't find or trust. Trend 3. Ecosystem modularization. Move over monolithic insurance platforms. Here come composable, modular architectures. The panelist conversation reinforced that data solutions are not separate from modernization strategy. They're foundational to making modular ecosystems work and unlocking value across the organization. Rather than rebuilding capabilities system by system, modular ecosystems allow insurers to expose reusable data and analytics building blocks across the enterprise and with partners. This shift also makes it far easier to integrate best-in-class solutions without ripping and replacing core systems like a data catalog, a feature store, or a fraud detection model. Trend 4. Self-directed systems. * Automated underwriting for simple risks * Real-time fraud blocking * Intelligent claims triage These solutions currently exist, but AI is making them more intelligent, adaptable and accessible across lines of business. As these self-directed systems become table stakes, guardrails must be audited to ensure proper decisioning and transparency. "Policy as code," continuous monitoring, explainability frameworks, and clear human escalation paths aren't bureaucratic overhead. Full stop. They are essential in making automation sustainable and auditable. Platforms like Atlan and Databricks are valuable in ensuring self-directed systems perform as intended so they don't drive adverse business outcomes or fail auditability and regulatory requirements. Thank you again to its generous panelists and its community of practitioners at the Data & AI Summit. It was great to be in conversation! | SET AI IN MOTION | | Move AI from experimentation into everyday operations. * Adaptable foundations * Human+AI workflows * Measurable outcomes * $8B+ in customer value * 3x average ROI |
Fivetran selected by Oakbrook Finance to support real-time lending decisions and AI-ready data operations. Fivetran, the data foundation for AI, today announced that specialist UK consumer lender Oakbrook Finance has selected the company to centralise and govern data across its analytics estate. The deployment supports customer insight, product development, marketing effectiveness and machine learning workflows, while saving Oakbrook's data engineering team approximately three months of build time. Founded in 2011, Oakbrook is a specialist consumer lender serving near-prime and non-prime borrowers across the UK who are typically overlooked by mainstream banks. The business has lent more than £1.5 billion to UK customers and currently serves around 140,000 active borrowers. A significant share of applications comes through price comparison platforms, where Oakbrook typically has less than 10 seconds to ingest data, run its models, and return an offer. "Our data engineering team is lean and deliberately focused. Before Fivetran, a meaningful chunk of their time was spent maintaining pipelines rather than building things that move the business forward," said Ed Ball, Head of Data and Security at Oakbrook. "Fivetran has let us connect data sources we wouldn't have reached otherwise, which has materially expanded what we can analyse and model, from customer behaviour through to how our products perform in the market. That's directly fed into work like the launch of OakbrookOne, our debt consolidation product." To meet these requirements without increasing operational overhead, Oakbrook adopted an automated approach to data integration. Fivetran enables the company to centralise and govern data from multiple sources, supporting an open data infrastructure model in which data remains accessible, consistent, and under Oakbrook's control. "Financial services organisations are under pressure to move faster while maintaining strict governance and control over their data," said Alex Cresswell, Regional Vice President, Northern Europe, Middle East and Africa (NEMEA) at Fivetran. "An open data infrastructure allows them to do both, by ensuring data is accessible, governed, and usable across systems without adding operational complexity." Fivetran now ingests around 5 million rows of data for Oakbrook each month, creating a consistent, governed data layer across the business. This supports improved customer insight, application development, and marketing effectiveness, including the launch of new products such as OakbrookOne, a debt consolidation loan product that has so far enabled more than £50 million in consolidation lending, with customers saving an average of £110 a month and over 40 percent receiving same-day settlement of their existing debts.