Top Data Engineer Jobs

Designs and governs data architecture for internal applications and business intelligence systems. Owns database architecture, data modeling, warehouse migration, schema standards, and operational versus analytical workload separation. Builds and monitors batch and event-driven pipelines using SQL, PostgreSQL, and Python; supports ETL/ELT across varied sources. Advises technology teams on data-platform decisions, reporting, performance, security, recovery, and architectural tradeoffs. The role is onsite in Sacramento and requires substantial production database and pipeline experience.

Build and maintain production-grade batch and streaming data pipelines in Databricks using PySpark and SQL. Develop ingestion frameworks, data models, APIs, data quality checks, monitoring, and Medallion Architecture across bronze, silver, and gold layers. Integrate diverse data sources, optimize Spark workloads, document data lineage and architecture, and collaborate with engineering and federal stakeholders. An active Secret clearance or higher is required.

Lead the technical direction and production operations of Handshake’s data platform, including Airflow orchestration, ingestion, batch and streaming pipelines, BigQuery, Kubernetes, and cloud infrastructure. Build reliable workflow development and deployment paths, improve data resilience and observability, establish data contracts, support AI workloads, troubleshoot incidents, and mentor engineers across application, analytics, ML, and cloud teams.

Develop and maintain data transformation pipelines, data models, dashboards, visualizations, and self-service analytics tools. Clean, validate, and analyze complex datasets; translate business needs into technical solutions and actionable insights; document workflows; and communicate findings to technical and non-technical stakeholders. The role requires Kibana expertise, strong ETL and data pipeline knowledge, collaboration skills, an active TS/SCI clearance, and Security+ certification.

Own Tennr’s end-to-end data engineering function, building reliable pipelines from application and EHR systems into Snowflake. Lead healthcare data discovery and integration across multiple systems, establish Snowflake security and governance, develop data models, and implement monitoring and alerting. Explore AI-enabled methods for extracting insights from unstructured clinical referral documentation while collaborating with internal teams and healthcare stakeholders.

Designs, implements, and operates secure data management systems supporting intelligence missions. Develops data architectures, storage and integration strategies, databases, pipelines, and visualization capabilities. Optimizes data throughput and query performance, maintains certified environments, supports data science and IT activities, evaluates emerging technologies, and reports performance indicators to leadership. Requires active TS/SCI clearance with polygraph and U.S. citizenship.

Designs, develops, and optimizes scalable data platforms, pipelines, ETL/ELT workflows, and Lakehouse architectures using Azure Databricks, Spark, and cloud services. Builds data models, quality and governance frameworks, integrations, and secure access controls. Optimizes workloads, supports streaming and analytics initiatives, troubleshoots pipelines, and contributes to CI/CD, DevOps, infrastructure automation, documentation, and technical leadership.

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

Lead design and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

Designs and owns Python backend services, scalable data pipelines, Spark processing workflows, data models, ETL/ELT systems, and data distribution patterns. Manages Terraform-based cloud infrastructure, ensures data quality and observability, leads technical workstreams, performs production root-cause analysis, and mentors junior engineers. Collaborates with product, data, and platform leaders to deliver reliable, scalable solutions.

Design, develop, and support scalable ETL frameworks and data integration pipelines for complex healthcare datasets in Oracle Exadata environments. Responsibilities include data mapping, transformation, performance tuning, testing, troubleshooting, automation, documentation, data profiling, validation, reconciliation, and quality assurance. The role also supports secure coding standards, code reviews, operational reliability, and integration with modern cloud data platforms.

Build and scale data products supporting analytics, engineering services, and AI/ML models. Responsibilities include optimizing queries and data pipelines, developing data models and governance processes, administering ETL/ELT and reverse ETL orchestration, and creating documentation and training. The role also involves mentoring, interviewing, and hiring data engineers while helping define the organization’s data engineering strategy.

Lead Notion’s Data Foundations team by setting multi-quarter platform strategy, developing engineers and technical leaders, and guiding complex initiatives across data lakes, streaming, distributed compute, governance, and reliability. Drive enterprise-ready capabilities including data residency, access controls, lifecycle management, and key management. Improve Kafka, Debezium, S3/Iceberg, Spark, EMR, and Athena scalability, reliability, usability, and cost efficiency while partnering across engineering, AI, search, infrastructure, and security teams.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Leads large-scale data engineering initiatives, overseeing end-to-end delivery and mentoring junior engineers. Designs scalable, secure data architectures; automates data ingestion, transformation, and deployment; monitors pipelines; and resolves technical issues. The role requires advanced distributed data processing, data modeling, data warehousing, cloud platforms, analytics tools, CI/CD, DevOps, SQL, Python, and data pipeline technologies.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Designs, implements, maintains, and secures Azure-based data platforms. Develops ETL/ELT pipelines, data warehouses, data lakes, data models, and cloud integrations. Administers Azure data services and databases, including performance tuning, backup, recovery, high availability, and disaster recovery. Automates operational tasks, supports CI/CD, monitors platform performance, resolves production incidents, and provides technical support to business and development teams.

Develop and maintain cloud-native ETL pipelines and modernize the firm’s data architecture using Azure, Databricks, and Azure Data Factory. Build governed, reliable data products supporting reporting, analytics, automation, and AI use cases. Collaborate with technical and business stakeholders on data quality, security, lineage, access controls, testing, DevOps, and infrastructure-as-code practices.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and operate production data pipelines, CDC streams, and Snowflake Bronze-layer infrastructure using Python, SQL, Redpanda, and Dagster. Maintain source adapters, historical snapshots, schema-drift detection, deduplication, data-quality checks, reconciliation, and observability. Collaborate on Silver-layer modeling, governance tagging, CI/CD, containerization, and HVR-to-Redpanda migration. Participate in incident response and develop deeper ownership of orchestration, streaming, data governance, and dbt-based layered modeling.

Build and operate secure batch and streaming data pipelines across structured, semi-structured, and legacy sources. Responsibilities include data ingestion, integration, quality validation, governance, monitoring, alerting, workflow orchestration, deployment automation, troubleshooting, and provenance management. Collaborate with engineering, cloud, data science, security, architecture, and customer teams to deliver reliable data products and AI-enabled workflows in regulated environments.

Designs, develops, and maintains scalable data pipelines, ETL/ELT workflows, and data platforms for Intelligence Community missions. Transforms structured and unstructured data using Python, SQL, Scala, or Java; supports ingestion, processing, monitoring, reliability, and data quality. Collaborates with analysts, developers, and data consumers in Agile teams, troubleshoots complex data environments, and translates mission requirements into technical solutions. The role is on-site in Chantilly, Virginia and requires an active TS/SCI clearance with polygraph.

Build and optimize CI/CD pipelines, cloud infrastructure, Infrastructure as Code, containerized environments, and automated testing processes. Design, maintain, and optimize large-scale data pipelines using Hadoop, HDFS, Apache NiFi, Spark, and Accumulo. Improve distributed-system performance, data reliability, monitoring, deployment workflows, and operational resilience while collaborating with engineering teams.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.