Top Data Engineer Jobs

Architects and builds enterprise data infrastructure connecting subscribers, content, advertising, and digital products. Leads distributed systems, APIs, event streams, real-time pipelines, Snowflake warehouses, dbt models, backend services, and machine-learning infrastructure for personalization. Establishes standards for observability, security, reliability, testing, and cost efficiency while providing technical leadership across AWS, containerized services, architecture, mentorship, and incident response.

Leads data engineering initiatives embedded with infrastructure teams. Designs governed data products, integrations, data contracts, quality controls, metadata, lineage, and standardized data models. Reconciles infrastructure asset inventories, resolves data discrepancies, and partners with engineering, product, and governance stakeholders to improve data completeness, accuracy, interoperability, security controls, and enterprise analytics.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include automating ingestion, AI processing, quality checks, monitoring, workflows, and data access; partnering with Data Operations and GIS teams; and improving AWS processing performance and cost. The role requires end-to-end ownership, including system design, operation, and on-call support.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include data ingestion, AI processing, quality monitoring, workflow automation, and AWS cost and performance optimization. The role partners with Data Operations, GIS, product, AI, and analytics teams and requires end-to-end ownership, including on-call support.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Administer and optimize Oracle application databases while developing data models, warehouses, ETL pipelines, and analytics solutions across Oracle and Snowflake. Build scalable Python and SQL workflows, support Oracle Analytics Server, manage MongoDB use cases, automate monitoring, and implement CI/CD. Ensure data quality, governance, security, high availability, disaster recovery, and performance across enterprise data platforms.

Designs and maintains reliable data pipelines and processing systems for high-volume telecommunications and digital evidence data. Builds data infrastructure supporting AI models and analytics, develops schemas and data models, optimizes performance and scalability, and monitors data quality. Collaborates with AI, data science, and software engineering teams, documents architecture, and follows secure practices for sensitive law enforcement data.

Build scalable, governed analytical data products, semantic layers, reusable data models, certified datasets, and AI-ready analytics foundations. Perform validation, quality checks, optimization, documentation, and lineage management. Collaborate with Analytics, Product, Engineering, Data Engineering, and business stakeholders on project planning and metric governance. Improve self-service reporting, dashboard performance, automation, and data consistency while mentoring junior team members.

Build and maintain scalable data platforms, lakehouses, warehouses, ETL pipelines, Spark workflows, and AI/ML-integrated data solutions. Design dimensional models, implement governance and monitoring, optimize queries, integrate disparate data sources, and support KPI dashboards. Collaborate with engineering, product, DevOps, DBA, and security teams while mentoring engineers and analysts. Automate infrastructure and deployments using AWS, Terraform, and CI/CD practices.

Designs and maintains scalable data pipelines and architectures supporting AI and machine learning workloads for Army intelligence operations. The role develops data models, ensures data quality, availability, security, and governance, optimizes workflows for real-time analytics, integrates AI/ML solutions, and collaborates with internal and external stakeholders. Expertise with Palantir Foundry, programming, ETL, databases, and large language models is required, along with an active TS/SCI clearance and eligibility for TS/SCI with polygraph.

Build and operate data integrations, ETL pipelines, schemas, monitoring, and production services in a restricted, air-gapped government environment. Develop extensions, deploy software across low- and high-side systems, monitor data quality and pipeline health, troubleshoot incidents, perform root-cause analysis, and create runbooks. Serve as the on-site engineering liaison between the customer and Arlington-based engineering and DevSecOps teams. The role requires independent judgment, customer-facing field engineering experience, and operational ownership.

Design and build scalable cloud-native data platforms, batch and streaming pipelines, data lakes, and integrated data services. Support analytics and AI/ML workloads while implementing DevSecOps, infrastructure as code, and reliability improvements. Collaborate with data scientists, engineers, architects, consultants, and clients to solve complex data challenges and translate requirements into technical solutions. The role supports Defence, Government, and National Security projects and requires high-level security clearance and on-site work three to four days weekly.

Design, build, and manage scalable batch and real-time data pipelines, databases, and processing systems. Develop and administer Databricks and Snowflake environments, optimize data architecture and distributed processing, manage PostgreSQL and MySQL databases, and implement governance, monitoring, alerting, and troubleshooting practices. Collaborate with analytics, engineering, and product teams to deliver reliable data solutions, document workflows, and promote modern data engineering practices.

Build and operate scalable multimodal data pipelines from requirements through delivery. Own feasibility, planning, quality standards, annotation, processing, storage, cost tracking, and acceptance criteria. Coordinate engineers, annotation teams, vendors, and client-facing stakeholders while improving infrastructure, automation, tooling, dataset visibility, and data quality for AI and foundation model teams.

Leads the design and development of scalable, secure data architectures and production data pipelines. Builds batch and streaming ETL/ELT solutions, manages data quality and reliability, applies cloud-native technologies, and ensures compliance with privacy, residency, security, and financial regulations. Partners with technical teams and business stakeholders, defines technical strategy, evaluates technologies, and delivers software solutions. Preferred responsibilities include mentoring engineers, leading technical initiatives, and applying AI/ML technologies.

The role involves designing and building scalable data platforms, migrating data to the cloud, and implementing CI/CD practices while collaborating with cross-functional teams.

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.

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.

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.