Top Data Engineer Jobs

Builds and maintains Azure-based data infrastructure, including scalable ingestion pipelines, Databricks processing workflows, governance controls, data quality validation, and curated datasets for analytics and reporting. The role uses Python, PySpark, SQL, Azure Data Factory, Azure Functions, Logic Apps, and Azure Data Lake Storage Gen2, while partnering with IT, Finance, Operations, and Digital stakeholders. It is hybrid and requires several days in the Alexandria, Virginia office.

The Data Engineer Intern will support the development and testing of enterprise data pipelines during a 10-week internship. Responsibilities include integrating structured and semi-structured data, writing SQL and code for transformations, validating data quality, troubleshooting pipeline issues, documenting data models and mappings, participating in Agile ceremonies and code reviews, and presenting project outcomes. The role provides practical experience with cloud technologies, data engineering standards, analytics, and cross-functional delivery.

Designs and develops scalable enterprise data architectures, models, integration strategies, governance practices, and cloud data platforms. Builds optimized data pipelines for analytics, AI, and machine learning workloads, including preprocessing, feature engineering, real-time inference, and production model integration. Collaborates with architects, engineers, analysts, stakeholders, and distributed teams across client projects. The role is hybrid in Leawood, Kansas, with occasional client-site travel.

Build and deploy scalable data engineering solutions supporting AI and machine learning applications for national security customers. Responsibilities include designing ETL processes, ensuring data movement and storage, working with Databricks and relational databases, supporting cloud-native deployments on AWS, and collaborating with software engineers and data scientists in Agile Scrum environments. The role requires onsite work three to five days weekly and an active Secret clearance with willingness to obtain TS/SCI.

Build and support knowledge graph and data engineering solutions for classified defense and intelligence customers. Responsibilities include graph theory, data modeling, graph database development, querying with Cypher, SQL, and SPARQL, scripting in Python and shell, Linux development, and collaboration with software teams. The role requires onsite work in a SCIF and communication with analysts and senior stakeholders. Preferred experience includes AWS, object-based intelligence, classified-environment communications, and Security+ certification.

Build and maintain scalable data pipelines and platforms using Python, ETL procedures, APIs, and software for structured and unstructured data. Develop visualizations, dashboards, and lightweight web applications to communicate insights. Collaborate with analysts, developers, and data consumers in agile teams, conduct root cause analysis, and deliver technical solutions for mission-driven clients.

Build and maintain data infrastructure, data lakes, data marts, ETL processes, and data quality frameworks for analytics and operational insights. Develop complex Oracle and Snowflake SQL, optimize query performance, manage large datasets, support near-real-time analytics, document database architecture, and contribute to AWS-based deployments and continuous integration pipelines.

Builds and maintains data models, architectures, ETL workflows, integrations, normalized schemas, and data warehouses supporting CISA cybersecurity missions. Responsibilities include assessing source-system data quality, designing storage and transformation strategies, developing data integrations, documenting information architectures, and creating denormalized views for applications and visualization. The role collaborates with agency stakeholders and engineering teams in an Agile environment to support digital transformation.

Lead data modeling and ownership of critical pipelines supporting Block’s product data foundation. Build reliable, governed, and monitored datasets, data quality and lineage systems, experimentation infrastructure, and AI-assisted automation. Partner with product and engineering teams to translate business needs into end-to-end data solutions, participate in on-call support, and maintain pipeline SLAs.

Engineers enterprise data platforms across Snowflake, AWS, Oracle Database, and Exadata environments. Designs data architecture, replication, synchronization, ingestion pipelines, schema evolution, workload governance, high availability, disaster recovery, and performance optimization. Supports Oracle and Snowflake administration, query tuning, data modeling, QA readiness, project planning, technical strategy, risk management, and mentoring. Requires expertise with large-scale databases, Oracle replication technologies, and resilient data platforms.

Designs, builds, and supports complex ETL and data integration solutions, scalable data pipelines, enterprise data warehouses, and cloud-based data environments. Develops CI/CD pipelines, SQL procedures, batch and real-time processing, and automated testing. Troubleshoots production issues, improves reliability and performance, supports Agile delivery, advises leadership, collaborates with business partners, and mentors junior engineers.

Build and optimize data models, pipelines, monitoring, lineage, and data quality systems supporting Block’s product data foundation. Own critical data engineering solutions across their full lifecycle, ensure pipeline reliability through on-call support, and translate business and product needs into automated data solutions. Develop AI-assisted workflows and agents for data quality, issue resolution, and operational efficiency while partnering with engineering, product, data science, and machine learning teams.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Designs and develops scalable data architectures, pipelines, data warehouses, and analytics solutions across on-premise, cloud, and hybrid environments. Performs data wrangling and discovery, collaborates on business intelligence initiatives, mentors data engineers, contributes to technical capability development, and supports project planning and estimation.

Build and maintain data pipelines and scalable cloud infrastructure supporting analytics and machine learning. Integrate internal and external data sources, ensure data integrity, contribute to analytical investigations, document findings, and apply data governance, security, and performance optimization practices while collaborating with senior engineers and cross-functional teams.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

The role involves designing and managing data pipelines, developing canonical datasets, and ensuring data integration and compliance. Collaborate across teams to meet data needs and maintain data architecture.

Owns the data transformation layer powering agency reporting. Builds reliable analytical models, dimensional schemas, reporting architecture, monitoring, and alerting. Translates government and customer reporting requirements into reusable data models and tools, supports embedded analytics and recurring imports, and ensures authorization scoping prevents cross-organization data exposure. Partners across product and implementation teams while establishing data engineering practices and documentation.

Build and optimize batch and streaming data pipelines on Google Cloud for analytics and machine learning. Develop Python, SQL, Beam, and Spark transformations; deliver curated and ML-ready datasets; implement data quality, governance, security, monitoring, and schema management; optimize BigQuery; maintain Airflow DAGs; and manage Terraform-based infrastructure and CI/CD. Collaborate with architects, data scientists, ML engineers, and stakeholders while troubleshooting pipeline and data quality issues.

Build and optimize batch and streaming data pipelines for analytics and AI workloads using GCP services, Python, SQL, Beam, and Spark. Develop ML-ready datasets, implement data quality, governance, security, monitoring, and schema management, and optimize BigQuery performance. Maintain Airflow DAGs, Terraform infrastructure, and CI/CD pipelines. Troubleshoot production issues and collaborate with architects, data scientists, ML engineers, and stakeholders. The role requires U.S. citizenship, a bachelor’s degree, Public Trust eligibility, and 3–6+ years of experience.

Engineers and maintains enterprise telemetry ingestion pipelines, edge processing, data routing, filtering, masking, tagging, and transformation solutions. Develops Splunk technology add-ons, resolves parsing and timestamping issues, establishes secure transport pathways, optimizes storage and licensing, and documents data flows and taxonomies. Supports platforms including TAK, TRAX, Compass, CDS, and cloud environments while enabling threat hunting, compliance reporting, and DoD data visibility requirements.

Designs, builds, deploys, and maintains scalable data platforms and ETL/ELT pipelines. Processes structured, unstructured, batch, streaming, and event-based data while optimizing data lake architecture and cloud performance. Collaborates with analysts, engineers, developers, and data consumers on mission-driven projects. Responsibilities include data modeling, governance, cataloging, lifecycle management, automation, platform tuning, and developing data solutions for applications such as fraud detection, cancer research, and national intelligence.

Own end-to-end data engineering for a large-scale analytics and machine learning platform. Build ingestion, modeling, transformation, and workflow pipelines; integrate external systems; ensure reliability, scalability, and data quality; and support production machine learning workflows. Develop programmatic interfaces for AI applications and manage analytics engineering within a cloud data warehouse.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Build scalable data infrastructure, pipelines, streaming solutions, and governed data models supporting regulatory reporting, financial operations, analytics, and AI-powered applications. Partner with Engineering, Compliance, Analytics, and business teams to deliver reliable, auditable, cloud-based data products using modern distributed data technologies.