Senior Database Reliability Engineer
About the job
- At least 6 years of experience in Database Reliability Engineering, Database Engineering, Site Reliability Engineering, Platform Engineering, or a closely related role.
- Strong hands-on experience operating PostgreSQL at scale, preferably in cloud-managed environments.
- Strong experience with Elasticsearch, including cluster administration, performance tuning, scaling, shard management, and troubleshooting.
- Experience operating databases and stateful workloads on Kubernetes.
- Experience with Kafka or another distributed database or storage system.
- Strong Infrastructure as Code experience with Terraform or similar tools.
- Experience with at least one major cloud platform: Azure, AWS, or Google Cloud Platform, with preferred exposure to multi-cloud environments.
- Strong understanding of database reliability principles, including high availability, disaster recovery, backups, replication, capacity planning, and observability.
- Proficiency in scripting or programming using Python, Go, or a similar language.
- Experience automating repetitive operational tasks and building self-service infrastructure.
- Strong troubleshooting and incident-management skills, with the ability to diagnose complex distributed-system failures.
- Standardize and automate lifecycle management for a fleet of more than 1,000 PostgreSQL instances across Azure, AWS, and Google Cloud Platform.
- Build and maintain infrastructure using Infrastructure as Code and automation frameworks.
- Automate provisioning, configuration, patching, upgrades, backups, and operational workflows.
- Improve consistency and reliability across the PostgreSQL fleet while reducing manual operational effort.
- Work with Azure Database for PostgreSQL Flexible Server, Amazon RDS, and Google Cloud SQL.
- Evolve provisioning and configuration using Kubernetes-based and Terraform-driven workflows.
- Design, operate, and scale high-performance Elasticsearch clusters across Elastic Cloud and self-managed Kubernetes environments using ECK.
- Own the reliability and performance of Elasticsearch infrastructure supporting search, semantic retrieval, vector workloads, and high-volume operational data.
- Establish best practices for cluster sizing, capacity planning, shard management, upgrades, backups, monitoring, and disaster recovery.
- Support Elasticsearch deployments where PostgreSQL is the source of truth and environments where Elasticsearch is the authoritative datastore.
- Build automation to simplify provisioning and lifecycle management across Elastic Cloud and ECK.
- Improve security, connectivity, zone awareness, and operational resilience across multi-cloud deployments.
- Own the reliability, scalability, and performance of Kafka clusters supporting high-throughput, low-latency event streaming.
- Operate Kafka in self-managed Kubernetes-based configurations, including Strimzi and Kafka Operator, and managed configurations such as Confluent Cloud and Amazon MSK.
- Drive capacity planning, partition and topic design, replication strategy, and broker-level performance tuning.
- Establish and automate best practices for upgrades, rolling restarts, backups, disaster recovery, and schema evolution using tools such as Schema Registry.
- Build monitoring and alerting for consumer lag, broker health, in-sync replica status, and throughput to enable early incident detection.
- Partner with Software Engineering to ensure producer and consumer patterns, retention policies, and topic ownership scale as usage grows.
- Automate database provisioning and lifecycle operations so they become repeatable.
- Reduce operational toil across PostgreSQL, Elasticsearch, and Kafka.
- Enable database platforms to scale reliably with business and customer growth.
- Detect incidents early and resolve them through automation and observability.
- Maintain consistent and resilient database infrastructure across Azure, AWS, Google Cloud Platform, and private-cloud environments.
- Enable engineering teams to consume reliable database infrastructure without significant manual Database Reliability Engineering intervention.
- Experience with Kafka and fdb-kubernetes-operator.
- Experience with Elastic Cloud and ECK.
- Proficiency in Go for building automation, tooling, or operators; Python or another scripting language is a plus.
- Experience managing PostgreSQL through Kubernetes operators or custom resources.
- Experience with Kubernetes operators and building automation for stateful workloads.
- Experience operating databases across multi-cloud and private-cloud environments.
- Experience with observability platforms and database performance monitoring.
- Familiarity with distributed systems, data replication, consistency, and failure-domain design.
About the company
Cognite provides cloud-based data and AI platforms for industrial companies to digitize and optimize operations. Its flagship Cognite Data Fusion unifies data from OT, IT, and engineering systems to give a real-time, contextual view of assets by linking sensors, maintenance logs, and 3D models. Cognite Atlas AI is a low-code platform that lets engineers build AI agents for predictive maintenance, anomaly detection, and production optimization without heavy coding. The company focuses on asset-heavy industries and aims to accelerate industrial digitalization by turning data into measurable value, improving safety, reliability, and efficiency.
Company Size
501-1,000
Company Stage
Acquired
Total Funding
$225.3M
Headquarters
Tempe, Arizona
Founded
2016
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Simplify's Take
What believers are saying
- AGC selected Cognite in September 2026 for chemicals manufacturing data integration.
- LTTS launched a Cognite Center of Excellence in September 2026, adding delivery capacity.
- Cognite’s 2026 revenue exceeded $170 million, with ARR bookings growing 36 percent.
What critics are saying
- Schneider’s acquisition remains pending regulatory approvals, delaying employee liquidity and strategy clarity.
- AVEVA integration can commoditize Cognite inside Schneider, weakening standalone brand and pricing power.
- Competition from Snowflake, NVIDIA, and enterprise ERP vendors compresses differentiation and margins quickly.
What makes Cognite unique
- Cognite Data Fusion contextualizes OT, IT, and engineering data for heavy-asset workflows.
- Atlas AI and Integrated Supply Chain extend Cognite from plants into enterprise decisions.
- Schneider Electric’s $3.1 billion acquisition validates Cognite’s industrial AI differentiation and market position.
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Benefits
Competitive Compensation + 401(k) with employer matching
Health, Dental, Vision & Disability Coverages with premiums fully covered for employees and all dependents
Unlimited PTO + flexibility to enjoy it
Paid Parental Leave Program
Learning & Development Stipends
Global Mobility & Exchange Program
Company Paid Friday Lunch via DoorDash + Fully Stocked Fridges in the offices
Growth & Insights and Company News
6 month growth
0%1 year growth
0%2 year growth
0%Taming Industrial AI Starts with Great Data. October 6, 2026 By Asha Suparna Technology Trends Cognite, a Gold Sponsor of ARC Advisory Group's 24th Annual ARC Industry Forum in Bengaluru, was represented by Ravi N. Mishra, Sr. Director - Sales (India), and Pawan Misra, Principal Designer for Data Integrations, Cognite India. In their presentation, Taming the Wild State of Industrial AI Starts with Great Data, they explored how contextualized industrial data can provide the foundation needed to move from basic AI assistants toward agentic workflows that support real operational tasks. Industrial companies have no shortage of AI models, copilots, and agents to experiment with. The harder problem is giving those systems the right industrial context. Operational information is typically distributed across historians, SCADA and control systems, maintenance applications, engineering documents, enterprise platforms, 3D models, and other repositories. Cognite's central argument is that AI model capability alone is not enough. Industrial AI depends on bringing these different forms of information together in a way that allows AI systems to understand the relationships between assets, processes, events, documents, and operational history. The presentation can be viewed on YouTube or here: Context is the bottleneck for Industrial AI. Cognite describes its own AI journey as progressing from general-purpose chat agents toward increasingly specialized and integrated workflows. An early phase used a single general-purpose chat agent, but the approach lacked the domain context and guardrails required for industrial applications. This progressed to task-specific agents designed for defined use cases, followed by deep research and ambient agents capable of handling more complex analytical tasks. The next step is agentic workflows that combine agents with generated applications and retain humans in the loop. Examples include root cause analysis, work package creation, operations summarization, quality investigation, and batch analytics. However, building the agent itself is only part of the challenge. Cognite identifies context as the bottleneck. Industrial data remains multimodal and siloed, while document-based retrieval-augmented generation and vector search may struggle to find the specific information required for complex industrial questions. The challenge is not simply locating data, but understanding how different pieces of information relate to one another. Cognite identifies grounding and industrial context, rather than AI model capability alone, as a key bottleneck when operational data remains distributed across multiple systems and formats. Building an Industrial Knowledge Graph. Industrial environments generate many different types of information. An individual asset may be represented in an equipment hierarchy, produce real-time time-series measurements, appear in engineering drawings and maintenance records, have associated work orders and inspection histories, and be represented in 3D models or images. These sources frequently use different naming conventions and schemas. Cognite Data Fusion is designed to connect these sources and contextualize them within an Industrial Knowledge Graph. The presentation showed information spanning assets, time series, events, engineering files, maintenance work orders, 2D and 3D information, and visual data being linked into a common industrial context. Cognite also emphasizes governance around that data foundation. Governed access controls determine who can access information, while an open, stable API allows organizations to use their own applications, agents, and AI models against the contextualized data. According to Cognite, its industrial data foundation can connect more than 100 IT, OT, and engineering technology sources, with the knowledge graph providing the layer that ingests, contextualizes, and governs the information. Cognite's Industrial Knowledge Graph connects operational, engineering, maintenance, and visual information to provide applications and AI agents with a common industrial context. From AI assistants to agentic workflows. Once that contextual foundation exists, AI can begin supporting more specific industrial workflows. Cognite identifies root cause analysis, work package generation, and shift handover as three practical starting points. The company says its root cause analysis approach can reduce time-to-cause from hours to minutes, while its shift-handover application can reduce meeting duration by 25 percent or more. The broader goal is not simply to make existing workflows faster. Cognite argues that agentic systems can begin changing how work is organized. Rather than an operator repeatedly searching multiple systems for information, an agent can monitor assets, retrieve relevant context, assemble information, and surface the cases that require human attention. This can also extend to application creation. Cognite Flows is designed to allow domain experts to create workflows, visualizations, and applications using AI, reducing their dependence on separate development teams for every new operational requirement. The human remains part of the process, but the distribution of work can change as agents take on more of the information gathering and orchestration. Root cause analysis in practice. Pawan Mishra demonstrated this approach through an AI-assisted root cause analysis workflow. The investigation began with a specific equipment problem and used multiple agents to retrieve and analyze relevant information. The workflow brought together equipment information, failure modes, time-series data, engineering drawings, maintenance history, work orders, notifications, shift reports, and other contextual data from the industrial data foundation. The agent could then systematically evaluate potential causes, document the supporting evidence, and generate an RCA report. Importantly, the workflow was designed to expose the evidence used in reaching a conclusion. This allows engineers to review the information and determine whether the agent's analysis is appropriate rather than treating the AI output as an unexplained answer. The workflow can also be adapted to an organization's existing RCA methodology, including established investigation and reporting processes. According to Cognite, the RCA demonstrated during the presentation could run in approximately 10 minutes. The company said the customer-specific workflow itself was built in approximately two and a half days once the underlying knowledge graph was already available. That qualification is important: the speed of building the AI workflow depends heavily on having the contextualized industrial data foundation already in place. Building the foundation for Industrial AI at scale. Cognite sees the same data foundation supporting increasingly sophisticated industrial AI. The company is currently collaborating on the training and testing of a time-series foundation model using a large multimodal and contextualized industrial dataset. The presentation cited 93 trillion time-series data points and 4.4 billion data-modeling instances as part of that foundation. The longer-term objective is to enable AI models that can work across different industrial assets while remaining grounded in operational context. For industrial organizations, however, the immediate lesson is more fundamental. The effectiveness of an AI agent depends on the quality of the information available to it and the relationships established between different sources of industrial data. Building additional copilots or choosing more capable models will not by itself solve fragmented asset hierarchies, inconsistent naming, disconnected engineering information, or inaccessible maintenance history. Industrial AI becomes more useful when those sources are connected, contextualized, governed, and made accessible to the systems that need them. As manufacturers and process industries move from isolated AI use cases toward agentic workflows, the competitive advantage may therefore come less from the model itself and more from the industrial knowledge foundation underneath it.
PTC expands AI and software capabilities with major acquisitions. October 4, 2026 PTC has announced the acquisition of Cognite and the £1.2 billion purchase of Shelly Group, enhancing its software and AI assets. This move signals a significant investment in technology that could impact data centers and AI infrastructure. InfraSale's audience should monitor how these acquisitions might influence market dynamics and competition in the tech sector. data centers ai infrastructure investment acquisition
LTTS and Cognite establish industrial-ai Centre of Excellence. September 25, 2026 L&T Technology Services and industrial-software company Cognite have entered a strategic partnership to jointly develop artificial-intelligence and data solutions for asset-intensive industries. The collaboration combines LTTS's engineering, manufacturing-operations and asset-lifecycle capabilities with Cognite's industrial data and AI platform, initially targeting oil and gas, chemicals, liquefied natural gas, consumer goods, mining and industrial manufacturing. LTTS has established a dedicated Cognite Centre of Excellence and certified an initial group of more than 50 engineers. The teams will build industry-specific applications spanning digital twins, predictive maintenance, operational intelligence and AI-enabled engineering workflows. The partnership will operate through LTTS's Sustainability segment and extend the Indian engineering-services company's Engineering Intelligence portfolio with Cognite's industrial data contextualisation and AI capabilities. The companies' programme is already tied to a production engagement rather than being limited to training or joint marketing. The first disclosed project involves one of the world's ten largest oil and gas companies. LTTS and Cognite are converting a manually executed mechanical-integrity assessment process into a contextualised digital workflow. The system is designed to give engineers structured access to relevant operational data, accelerate the identification of equipment risks and support actions intended to prevent unplanned downtime and safety incidents. The customer has not been named. Cognite's platform is built to consolidate and contextualise industrial information from engineering systems, sensors, maintenance records and operational applications. Its portfolio includes Cognite Data Fusion, the Atlas AI industrial-agent workbench and Cognite Flows for deploying operational workflows. LTTS plans to combine these capabilities with its EIOne framework and its domain experience in product engineering, manufacturing and asset management. The partners are focusing on moving industrial AI deployments from isolated pilots into repeatable workflows connected with existing plant and engineering systems. LTTS chief executive and managing director Amit Chadha said the partnership would address the need for interconnected, context-aware workflows as industrial customers seek to scale AI beyond experimentation. Cognite chief executive Girish Rishi said LTTS's domain and engineering capabilities would complement Cognite's industrial platform as AI becomes more deeply embedded in manufacturing operations. Headquartered in India, LTTS had more than 23,840 employees as of June 30, 2026. Its delivery network comprised 21 design centres, 30 global sales offices and 103 innovation laboratories. The company serves 69 Fortune 500 businesses and 57 leading engineering research and development customers across industrial products, medical devices, transportation, telecommunications, high technology and process industries. The dedicated centre, certified engineering cohort and initial customer deployment give the partnership a defined delivery structure. Its stated commercial scope covers the design, implementation and continuing operation of AI-enabled industrial applications, including systems that must work with complex legacy data and safety-sensitive physical assets. - Advertisement -
L&T Technology Services partners with Cognite to advance Engineering Intelligence with Industrial AI. Bengaluru, karnataka, India & phoenix, arizona, united states - business wire India. Published - September 25, 2026 08:06 am IST LTTS and Cognite partner to advance Engineering Intelligence with Industrial AI for asset-intensive industries. In the picture (L-R): Girish Rishi, CEO, Cognite, with Subrat Tripathy, Chief Growth Officer - Sustainability, L&T Technology Services. Highlights: * Combine LTTS' engineering expertise with Cognite's Industrial AI and Data capabilities to deliver scalable, industry-specific solutions across asset-intensive sectors. * Established a dedicated Cognite Center of Excellence and certified 50+ LTTS engineers to accelerate Industrial AI adoption. L&T Technology Services (BSE: 540115, NSE: LTTS), a global leader in Engineering Intelligence Solutions & ER&D Consulting Services, today announced its strategic partnership with Cognite, a global leader in Industrial AI. Through its Sustainability segment, LTTS will work with Cognite to develop and deliver joint industrial, AI and data for asset-intensive industries, extending LTTS' Engineering Intelligence portfolio with Cognite's Industrial AI capabilities. As part of the engagement, LTTS has established a dedicated Cognite Center of Excellence, certified an initial group of 50+ engineers and is co-developing industry-specific solutions to benefit customers across Oil & Gas, Chemicals, LNG, CPG/FMCG, Mining, and Industrial Manufacturing sectors globally. The collaboration brings together LTTS' expertise across engineering, manufacturing operations, and asset lifecycle management and Cognite's industrial AI and data capabilities. The partnership between LTTS and Cognite will help accelerate the deployment of AI-powered industrial applications, digital twins, predictive maintenance solutions, and operational intelligence platforms across asset-intensive enterprises. As part of their first joint project, LTTS and Cognite have already begun working with a global, top ten oil and gas company, to transform its manual mechanical-integrity process into a contextualized, scalable digital workflow that lets engineers work more efficiently, identify risk faster, and ultimately prevent downtime and safety events. "Cognite's growing scale across industries complements LTTS' domain and engineering expertise," said Girish Rishi, CEO of Cognite. "We are excited and looking forward to pairing up with LTTS' Engineering Intelligence approach to drive value outcomes to our joint customers as AI becomes a mainstay of modern manufacturing across the world." "As industries increasingly look to scale AI beyond experimentation, the need for connected and contextual industrial workflows has become critical. Our partnership with Cognite strengthens LTTS' ability to deliver Engineering Intelligence by combining deep engineering expertise with AI-native industrial experiences. We are excited about the potential for joint value creation by integrating Cognite's capabilities with LTTS' EIOne, enabling us to co-create solutions that address complex industrial challenges and accelerate the journey from AI pilots to production. Together, we will help enterprises drive greater productivity, asset performance, operational resilience, and business value," said, Amit Chadha, CEO & Managing Director at L&T Technology Services and Member of Nasscom Executive Council. About L&T Technology Services ltd. L&T Technology Services (LTTS) is a global leader in Engineering Intelligence Solutions & ER&D Consulting Services. A listed subsidiary of Larsen & Toubro (L&T), we offer design, development, testing, and sustenance services across products and processes. Purposeful. Agile. Innovation. is how we drive growth across the Mobility, Sustainability and Tech segments. Our customer base includes 69 Fortune 500 companies and 57 top ER&D companies across industrial products, medical devices, transportation, telecom & hi-tech, and process industries. Headquartered in India, we have over 23,840 employees across 21 global design centers, 30 global sales offices, and 103 innovation labs, as of June 30, 2026. For additional information about L&T Technology Services log on to https://www.ltts.com/. "This is a company press release that is not part of editorial content. No journalist of The Hindu was involved in the publication of this release." Published - September 25, 2026 08:06 am IST
Element Science announces key leadership hires. Key leadership hires bring deep medtech scaling expertise to advance the Jewel Patch Wearable Cardioverter Defibrillator Aug. 26, 2026 - Element Science, the creator of the Jewel Patch Wearable Cardioverter Defibrillator (Patch-WCD) to monitor and protect patients at high-risk of sudden cardiac arrest (SCA), has announced several appointments to its executive team. These hires bring extensive industry experience across commercial strategy, operations, finance, and human resources to drive the company's next phase of growth. "These appointments reflect our continued investment in building a world-class leadership team with the expertise needed to execute our strategy," said said Doug Devine, Chief Executive Officer of Element Science. Paul, Mark, Eric and Cheeka each bring proven track records from some of the medtech industry's most successful companies. Together they bring decades of experience scaling innovative companies, and will strengthen its ability to execute, innovate and expand access to Jewel, while advancing its mission to redefine care for patients at risk of sudden cardiac arrest." Executive Appointments include: * Paul Mandina, Chief Revenue Officer - He has more than 20 years of medtech commercial leadership experience. Most recently, he served as VP of U.S. Sales at Medtronic. * Mark Hughes, Chief Operating Officer - Hughes joins Element Science from iRhythm, where he spearheaded operations through major commercial scaling. * Eric Samuels, Chief Financial Officer - He has extensive financial and operational experience from previous leadership roles at iRhythm and Intel. * Cheeka Trikha, Head of People and Organization - Trikha joins from Cognite, where she led People Business Partnering, following senior HR leadership roles at SAP, iRhythm, Confluent and Cognite.