Top Data Engineer Jobs

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Build and maintain scalable, resilient data systems and analytics platforms using Python, SQL, Spark, dbt, Kafka, cloud services, and modern data infrastructure. Design distributed systems, optimize data processing and storage, develop reusable data components, and support reporting solutions. Lead architecture and design sessions, code reviews, CI/CD, monitoring, and DevOps practices while collaborating cross-functionally and mentoring engineers.

Designs, builds, and owns enterprise data infrastructure supporting analytics, machine learning, and AI applications. Responsibilities include developing scalable ETL/ELT pipelines, API and database integrations, data models, ingestion frameworks, lake and warehouse architectures, Power BI reporting assets, data quality monitoring, governance, lineage, and reproducibility practices. The role partners with data scientists, machine learning engineers, business teams, and IT while supporting healthcare data environments and third-party data negotiations.

Build and maintain production batch and streaming data pipelines, reusable ingestion and transformation components, and shared engineering patterns. Collaborate with platform and domain teams to translate requirements into secure, performant, testable, and observable pipelines. Integrate data quality, governance, monitoring, orchestration, incident response, and CI/CD practices while supporting documentation and enablement for common data engineering capabilities.

Leads a data engineering team and supervises one Data Engineer while designing, maintaining, and optimizing reliable data pipelines and infrastructure. The role integrates internal and external data sources, supports SaaS analytics dashboards and connected platforms, improves data-store performance, and ensures infrastructure quality, security, and uptime. Requires proficiency in Python, databases, distributed processing, orchestration, AWS, and machine learning.

Designs and maintains data warehouse architecture, APIs, ETL pipelines, database objects, and data extracts from sources such as NetSuite, IRI, and point-of-sale systems. Develops Power BI dashboards and supports analysts with reporting. Ensures data quality, security, governance, and performance while researching large datasets and delivering AI-enabled analytics. Collaborates with business stakeholders and manages third-party analytics resources.

Maintain and administer Microsoft SQL Server databases supporting healthcare research administration, compliance, reporting, and operations. Design schemas and tables; develop and optimize stored procedures, queries, scripts, utilities, and reports; manage scheduled jobs; monitor performance, security, backups, recovery, and data integrity; troubleshoot database and application issues; coordinate with web developers; document solutions; and provide technical recommendations to research leaders.

Build Hyperbolic Labs’ data infrastructure from the ground up, owning the data warehouse, ingestion pipelines, data modeling, reliability, governance, monitoring, troubleshooting, and performance tuning. Partner with engineering and analytics teams to support growing products, data sources, BI, and reporting. The role requires deep PostgreSQL expertise, large-scale data experience, and independent ownership of production data systems.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.

Designs and maintains enterprise data models, metadata, taxonomies, governance standards, quality rules, lineage documentation, and integration mappings for NASA’s external engagement platform. Supports CRM workflows, analytics-ready data, dashboards, AI data preparation, and cross-agency interoperability. The role also translates business needs into technical data solutions and coordinates documentation and stewardship across teams.

Designs, builds, and maintains scalable data pipelines, relational databases, data integrations, and data warehousing solutions. The role develops ELT processes, ensures data quality and governance, troubleshoots pipeline and database issues, and documents workflows. It collaborates with Data Operations, Quality Engineering, Business Intelligence, architects, and subject matter experts to deliver reliable data assets aligned with business needs.

Build and operate production data pipelines and warehouse models for a global energy trading platform. Develop Python pipelines orchestrated with Prefect, create tested Snowflake models with dbt, migrate legacy Oracle systems, integrate diverse data technologies, and implement quality, freshness, monitoring, and alerting controls. The role also supports AI-ready data, AWS services, platform documentation, and direct collaboration with analysts and desk users.

Designs, implements, and sustains secure mission data pipelines for the Department of the Air Force’s DIFC2 program. Responsibilities include mission data architecture, interface and schema definition, ETL and transformation development, API integration, monitoring, documentation, and RMF compliance. The role requires collaboration with government, cybersecurity, and mission partners to deliver reliable, interoperable data flows in a defense environment.

Develop and maintain data pipelines for GEOINT datasets, transform and standardize data for machine learning environments, assist with data service development, document workflows, analyze data, and support data quality. Collaborate with senior engineers and scientists on Department of War artificial intelligence initiatives while communicating findings and adapting to changing priorities.

Build and maintain data pipelines, reporting solutions, Power BI dashboards, semantic models, and KPIs. Clean, transform, validate, and reconcile data from ERP, Microsoft, Excel, third-party, and operational sources. Automate repetitive reporting, troubleshoot data issues, support recurring and ad hoc requests, document business logic, and collaborate with departments to deliver reliable reporting and process improvements.

Build and optimize data pipelines, backend services, forecasting systems, dashboards, and automated workflows supporting Starlink ground-network inventory, logistics, deployment, and reliability. Integrate ERP/MRP and logistics systems, manage hardware demand forecasting, improve supply-chain processes, monitor operational metrics, and support emergency deployments. The role requires collaboration across engineering, infrastructure, logistics, and program teams, with up to 25% travel and occasional extended or weekend hours.

Lead the modernization of a healthcare data platform from an MSSQL XML object store to a scalable AWS architecture. Evaluate AWS storage and query services, design SQL reporting layers, architect data migration and metadata strategies, optimize performance and cost, and build CI/CD and infrastructure-as-code pipelines. Partner with business stakeholders, analysts, and C#/.NET engineers to deliver accessible data models and reliable integrations. Document architecture, data flows, and migration processes while supporting healthcare data and compliance requirements.

Designs, builds, maintains, and scales enterprise data infrastructure for ingestion, processing, extraction, and analysis. Responsibilities include ETL development, data cleansing and normalization, data modeling, warehousing, anomaly detection, statistical modeling, and natural language processing module development. The role also troubleshoots complex data systems and fulfills data-related business requests.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Supports implementation and stabilization of inbound and outbound healthcare data processes. Ingests, transforms, validates, and reconciles Medicaid claims and eligibility feeds from Gainwell, Arcadia, DHCS, and other sources. Establishes data-quality, refresh, completeness, and reconciliation controls for PRISM, case creation, and reporting. Supports Atrezzo interfaces, data exchanges, production analytics, troubleshooting, root-cause analysis, stakeholder communication, and implementation workstreams.

Design, build, and maintain scalable data pipelines, warehouses/lakes, and platforms. Lead multidisciplinary teams to implement ETL/ELT (batch and streaming), optimize data lake performance, enforce data governance, and support analytics and ML use cases on cloud and on-prem environments.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage solutions supporting intelligence operations. Ensures data quality, governance, security, compliance, and performance across structured and unstructured intelligence datasets. Documents schemas and data lineage, collaborates with analysts and data scientists, and participates in technical and mission-planning forums.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage for intelligence operations. Ensures data quality, governance, security, compliance, documentation, and lineage across multi-INT datasets. Collaborates with analysts and technical teams to translate intelligence requirements into secure solutions and participates in mission planning and coordination forums.