Top Senior Data Engineer Jobs

Leads the design, optimization, and governance of scalable data architectures and pipelines. Ensures data quality, integrity, security, and compliance; mentors junior engineers; conducts audits and validation; and collaborates on data governance. The role requires expertise in SQL, Python, dimensional modeling, Snowflake on Azure, Azure Data Factory, Databricks, ETL, and data warehousing, with preferred experience in Airflow, Kafka, healthcare, data lineage, and slowly changing dimensions.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

Own Serval’s complete data function as its first dedicated data hire. Build a unified warehouse, ETL/ELT pipelines, data models, quality and governance systems, documentation, and self-serve analytics. Partner with technical and business teams to define trusted metrics, consolidate product and SaaS data, and create AI-powered internal data tools. Select the data stack, establish engineering standards, and support scalable, cost-conscious infrastructure.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.

Designs, develops, and optimizes enterprise Snowflake data platforms, pipelines, data models, and ELT/ETL workflows. Builds reliable data lake and warehouse solutions, manages Snowflake performance and costs, implements data quality and governance practices, and supports cloud data environments on Azure or AWS. Partners with analysts, scientists, engineers, and business stakeholders to deliver scalable solutions, troubleshoot platform issues, influence architecture, and mentor junior engineers.

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Build and operate scalable Python-first ETL and ELT pipelines for genomic and operational data. Integrate LIMS, laboratory instruments, applications, and third-party sources while guiding architecture and engineering standards. Own data quality, lineage, observability, reproducibility, audit readiness, security, and regulatory compliance across hybrid and cloud environments. Work with SQL databases, workflow orchestration, containers, Kubernetes, AWS data services, distributed processing, streaming, bioinformatics workflows, and infrastructure as code.

Design, deploy, secure, and optimize enterprise database environments supporting mission-critical ArcGIS systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, data loading, and integration. The role operates across cloud, on-premises, and hybrid infrastructures and provides technical guidance to project teams and customers.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Architect and operate BILL’s enterprise data platform across ingestion, lake storage, batch and streaming processing, query, feature store, knowledge graph, and search layers. Drive platform-wide technical decisions, migrations, standards, reliability, cost efficiency, and self-service capabilities. Partner with ML, Risk, Payments, and Analytics teams to deliver scalable solutions, lead complex initiatives from inception through production, and mentor senior and staff engineers.

Build and manage high-volume analytics and production ETL/ELT systems. Partner with Data Science, Engineering, and Product teams to ensure reliable Search data logging and usage. Own data quality, observability, security, compliance, SLAs, scalability, performance, and cost efficiency across the data lifecycle. The role requires expertise in Python, SQL, data modeling, distributed data technologies, massive datasets, and data pipeline optimization.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Build and maintain IICS ETL/ELT pipelines, develop Amazon Redshift data models, and use SQL and Python for data transformation and automation. Create Power BI reporting models, implement data quality checks, document pipelines, and collaborate with client stakeholders and engineering teams. The role requires autonomous work, strong communication, and the ability to translate technical solutions into business context, with bonus value for Veeva or life sciences experience.

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.