Top Data Engineer Jobs

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.

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.

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.

Designs, develops, and maintains scalable data warehouses, data lakes, platforms, and ETL/ELT pipelines. Builds batch and streaming workflows, processes structured and unstructured data, optimizes cloud storage and pipeline performance, and implements data governance and lifecycle management. Guides multidisciplinary teams in agile environments while delivering mission-focused data engineering solutions.

Design, build, optimize, and operate scalable ETL/ELT pipelines supporting petabyte-scale legacy-to-cloud data migration. Develop data models, mappings, schemas, and data quality processes for structured and unstructured datasets. Improve automation, performance, reliability, and fault tolerance while supporting AWS security and compliance requirements. Collaborate with QA, Cloud, DevSecOps, and migration teams; document workflows; track migration progress; and mentor engineers. This is a full-time, on-site role in Huntsville, Alabama requiring active Top Secret clearance.

Build and maintain databases, data warehouses, and ETL/ELT pipelines for client data solutions. Consult with clients to translate technical needs into data architectures, ensure data quality and governance, optimize system performance, and support secure, compliant data integration. Collaborate with data integration engineers, business analysts, software engineers, and technical and non-technical stakeholders in a client-facing role.

Designs, builds, and operates scalable batch and streaming data pipelines on Databricks for federal missions. Responsibilities include developing Spark and Delta Lake solutions, managing clusters and workflows, implementing Unity Catalog governance and security, optimizing ETL/ELT processes, integrating CI/CD, supporting machine learning and advanced analytics, monitoring data quality, and collaborating with technical teams and stakeholders.

Designs and develops advanced data engineering and visual analytics applications using structured and semi-structured data. Responsibilities include building large relational databases, developing analytics for desktop and web applications, integrating systems with data warehouses, optimizing performance, and establishing database operation, programming, and security standards. The role requires expertise in Python, Java or Scala, AWS, NLP, ML model evaluation, Git, and GitLab CI/CD, plus an active Top Secret/SCI clearance and potential CI polygraph eligibility.

Develops geospatial and enterprise data solutions supporting national security analytics. Responsibilities include integrating and governing data, managing metadata and metrics, querying databases with SQL, accelerating large-scale transactions on GPU architectures, and applying Python data science and machine learning libraries. The role also produces assessments, briefings, reports, and intelligence products while coordinating projects, personnel, working groups, and cross-community stakeholders. An active TS/SCI clearance with SSBI is required.

Build and manage complex ETL and data pipelines, data warehouses, monitoring, and alerting systems for a legal-focused infrastructure team. Lead cross-functional data engineering initiatives from planning through delivery, optimize large-scale data processing, identify reliability issues, and communicate with technical and non-technical stakeholders. The role also involves supporting consulting practice activities, documentation, thought leadership, and client-facing deliverables.

Leads enterprise data engineering architecture and SAP data conversion initiatives. Designs scalable ingestion, transformation, storage, and delivery patterns; builds BODS ETL workflows; manages extraction, cleansing, mapping, validation, reconciliation, and loading. Establishes automation, governance, quality, lineage, security, observability, and infrastructure-as-code practices. Coordinates analysts, SAP teams, engineers, business owners, vendors, and consultants through mock conversions, cutovers, rollback, and production operations while coaching teams and resolving critical issues.

Develop and maintain software, data pipelines, ETL workflows, automation, and databases supporting SAPRO's mission. Use R, SQL, Python, and Databricks for data extraction, transformation, manipulation, and analytical workflows. Troubleshoot and optimize pipelines, enhance software, define technical requirements, and collaborate with technical and non-technical stakeholders. The role requires at least three years of data engineering experience and an active Secret clearance.

Build and maintain enterprise data pipelines and integration workflows across on-premises and cloud systems. Ingest, transform, and stage structured and semi-structured data; write and optimize SQL queries, views, and stored procedures; implement data validation, monitoring, alerts, and logging; document pipeline logic; manage source control and code reviews; and support data operations through ticket response and on-call rotations.