Top Senior Data Engineer Jobs

Designs, builds, and operates production data pipelines on Databricks and Azure using Python, SQL, Spark, Delta Lake, dbt, and Dagster. Responsibilities include ingestion, medallion-layer transformations, dimensional modeling, ML datasets, data contracts, quality testing, governance, CI/CD, observability, performance tuning, troubleshooting, and on-call support. The role partners with architects, analysts, product owners, and business stakeholders to deliver reliable, reusable data products.

Build and maintain modern data-centric applications, pipelines, transformations, enrichment processes, provisioning layers, and user interfaces supporting clinical and operational healthcare processes. Analyze clinical data and report trends using cloud, big data, data science, and modern software development practices. Collaborate with Product, Platform, and Architecture teams, mentor Data Engineers, document solutions, manage source control, and research emerging technologies.

Leads enterprise data architecture and engineering initiatives, building scalable cloud data lakes, pipelines, warehouses, and integration frameworks. Designs dimensional models, ETL/ELT patterns, governance practices, and analytics solutions using Snowflake, Azure, AWS, and big data technologies. Oversees testing, QA readiness, documentation, project planning, risk management, and delivery across multiple platforms. Collaborates with business and analytics teams, advises senior leadership, resolves technical issues, and mentors junior engineers.

Build and operate reliable data infrastructure, including event-driven architectures, streaming and ingestion pipelines, API integrations, and automated workflows. Develop agentic systems that interpret events and trigger business actions, while ensuring data quality, observability, error handling, and safe execution. Partner with analytics teams, contribute to data architecture strategy, and manage multiple engineering projects. The role requires strong Python expertise and experience with real-time data systems, orchestration, and integrations.

Build and operate governed production data pipelines in Databricks using medallion architecture, Unity Catalog, data quality controls, lineage, and semantic models. Develop AI agents and retrieval-augmented applications grounded in enterprise data using Mosaic AI and model serving. Integrate enterprise systems through APIs and events, implement security and observability, and own architecture, testing, CI/CD, deployment, and production support.

Designs, develops, and optimizes scalable data models, cloud-native platforms, and batch and real-time pipelines for an AI/ML disability claims program. Responsibilities include data quality, governance, security, database optimization, ETL automation, observability, ML pipeline enablement, and cloud migration. The role mentors engineers, leads cross-functional initiatives, documents architecture, and ensures reliable, high-performance data systems supporting analytics and machine learning.

Develops Scala and Python data solutions for Medicare and Medicaid claims cost measures. Responsibilities include ETL, data analysis, SQL querying, validation, quality checks, documentation, requirements communication, and collaboration with external partners. The role participates in Scrum ceremonies, test meetings, and Agile planning while supporting CMS’s Quality Payment Program. It is remote within the United States, follows East Coast hours, and may require occasional travel for planning events.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, and Lakehouse architectures; supports SQL Server data loading and legacy C# .NET processes. Partners with analytics, software engineering, architecture, and product teams to ensure data quality and availability for reporting and machine learning. Troubleshoots pipelines and applies governance, security, compliance, documentation, and reliability best practices.

Leads the architecture, development, and operation of scalable cloud data platforms and complex ETL/ELT pipelines. Partners with senior stakeholders to define data strategy, architectures, and technical roadmaps. Mentors data engineers, establishes engineering and governance standards, and owns production reliability, data quality, observability, and incident response. Evaluates generative AI and agentic systems for data engineering while promoting responsible AI practices. Requires advanced Python, SQL, cloud, distributed processing, integration, DevOps, and data architecture expertise.

Build and operate scalable AWS data platforms, batch and near-real-time pipelines, ETL/ELT workflows, and governed data lake or lakehouse architecture. Lead data integration for organizational acquisitions, improve data quality and observability, and support Power BI reporting through curated datasets and semantic models. Partner with technical and business stakeholders across editorial, subscriptions, advertising, finance, marketing, and operations to deliver reliable analytics and reporting foundations.

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Leads the design and implementation of enterprise data platforms, pipelines, ingestion systems, and analytics products. Develops production-grade Python applications, manages AWS, Snowflake, and dbt environments, establishes engineering and governance standards, and improves observability, security, quality, and cost efficiency. Integrates disparate data sources and supports generative AI, RAG, and LLM applications while partnering with business, software, DevOps, and data science teams.

Designs, builds, and operates scalable batch and streaming data platforms using Spark, Python, SQL, Microsoft Fabric, and Lakehouse architectures. Develops ETL/ELT pipelines, dimensional models, data governance, observability, and CI/CD practices. Optimizes distributed workloads, supports AI and analytics use cases, leads technical design, and mentors engineers while partnering with architects, stakeholders, security, and governance teams.

Build, optimize, and support enterprise data pipelines, ETL/ELT workflows, semantic models, data warehouses, and business intelligence solutions. Responsibilities include production troubleshooting, performance tuning, dimensional modeling, data governance, documentation, stakeholder collaboration, mentoring junior team members, and contributing to data architecture modernization.

Lead enterprise-scale data architecture and engineering for Xometry’s Data Platform, including scalable batch and streaming pipelines, partner integrations, data models, governance, observability, and low-latency event-driven flows. Own the data plane connecting Xometry with Teamcenter and Designcenter, establish engineering standards, develop technical roadmaps, collaborate across teams, and mentor engineers. The role requires strong SQL, Python, cloud data warehouse, AWS, modern data tooling, CI/CD, testing, and enterprise integration expertise.

Own enterprise-scale data architecture and engineering for Xometry’s data platform, including real-time partner integration pipelines connecting manufacturing systems. Design scalable batch and streaming systems, data models, governance, observability, testing, and CI/CD practices. Lead multi-quarter technical roadmaps, collaborate across engineering, product, analytics, machine learning, and external partners, and mentor engineers. Support the DFM AI and IQE integration across Teamcenter, Designcenter, and related systems.

Build, deploy, and operationalize complex data pipelines and solutions using modern data platforms and ETL tools. Design data architectures, integrate sources, improve data quality, and support AI, machine learning, and business intelligence initiatives. Analyze and resolve complex data defects and incidents, apply governance and security practices, and serve as a data engineering subject matter expert. Guide team members, educate business users, communicate technical concepts, and provide technical leadership across enterprise data projects.

Build and lead Cognite’s enterprise data warehouse, semantic layer, data pipelines, integrations, and business data models. Use Azure, Microsoft Fabric, Power BI, Python, SQL, APIs, and Cognite Data Fusion to deliver reporting and AI-ready data solutions. Establish data governance, security, privacy, and quality standards while partnering with Finance, Sales, Marketing, HR, Legal, Engineering, and other stakeholders.

Build and operate production data pipelines across Oracle, Talend, and Apigee using SQL, PL/SQL, Java, and Python. Integrate acquisition and financial data, develop reusable data products and analytics datasets, maintain REST APIs, migrate legacy data, optimize workflows, and implement validation, governance, testing, documentation, and database design practices. Support Navy acquisition transformation in an IL-4/5 government environment.

Build and maintain Oracle EBS data replication and integration pipelines using GoldenGate and Qlik Replicate for Advana. Monitor performance, troubleshoot failures, optimize throughput and latency, document data flows, support BI dashboards, and ensure compliance with federal data governance and DoD cybersecurity requirements. Coordinate upgrades, patching, migrations, and collaboration with reporting, security, and IA teams.

Design, build, and maintain ETL/ELT pipelines and SSAS tabular models. Develop complex T-SQL queries and Power BI reports. Ensure data quality, optimize performance, and support migration of legacy systems into Microsoft BI environments while collaborating with stakeholders.