Top Data Engineer Jobs

Build and operate production data pipelines across Oracle, Talend, and Apigee using SQL, PL/SQL, Java, and Python. Integrate acquisition and financial data, develop reusable data products and analytics datasets, maintain REST APIs, migrate legacy data, optimize workflows, and implement validation, governance, testing, documentation, and database design practices. Support Navy acquisition transformation in an IL-4/5 government environment.

Build and maintain Oracle EBS data replication and integration pipelines using GoldenGate and Qlik Replicate for Advana. Monitor performance, troubleshoot failures, optimize throughput and latency, document data flows, support BI dashboards, and ensure compliance with federal data governance and DoD cybersecurity requirements. Coordinate upgrades, patching, migrations, and collaboration with reporting, security, and IA teams.

Builds and maintains reliable data solutions, datasets, transformations, and enterprise data warehouse infrastructure. Responsibilities include data quality profiling, cleansing, normalization, modeling, testing, CI/CD, performance optimization, anomaly analysis, and troubleshooting. The role partners with data architecture teams, supports reporting and KPI accessibility, and follows PII and organizational risk standards. It requires onsite work in West Bend, Wisconsin, with some hybrid flexibility.

Leads development of Navanta’s secure data backbone, including lakehouse architecture, bank-system ingestion, CDC pipelines, semantic modeling, data quality, reconciliation, observability, governance, and lineage. Partners with AI/ML, security, and platform teams to deliver reliable, queryable data products for financial institutions. Owns data contracts, access controls, regulatory data handling, pipeline SLAs, bank onboarding, and on-call reliability operations.

Design, build, and maintain ETL/ELT pipelines and SSAS tabular models. Develop complex T-SQL queries and Power BI reports. Ensure data quality, optimize performance, and support migration of legacy systems into Microsoft BI environments while collaborating with stakeholders.

Design and modernize scalable data engineering, BI, and data warehouse solutions. Build enterprise ETL pipelines using Azure, Databricks, Informatica PowerCenter, SSIS, and related technologies. Develop complex T-SQL, tune database performance, test ETL solutions, troubleshoot data issues, and collaborate cross-functionally. Provide technical guidance to Data Engineers while supporting data modernization, analytics, reporting, and operational efficiency.

Supports government space and counterspace intelligence operations by collecting, integrating, validating, maintaining, and analyzing large datasets. Develops automated data workflows, scripts, databases, dashboards, reports, and visualizations; establishes data quality and governance practices; analyzes RF and signals data; and supports collection management, mission planning, operational reporting, and readiness assessments. Collaborates with government stakeholders, mission partners, and technical teams.

Designs, implements, secures, monitors, and optimizes AWS cloud-native data engineering solutions supporting analytics, AI/ML, LLMs, and agentic services. Builds ingestion, ETL, transformation, data-store, API, search, monitoring, FinOps, and security capabilities. Prepares model-ready datasets, applies DoD Zero Trust and NIST controls, supports authorization activities, documents architectures and costs, and collaborates with government, data science, cybersecurity, and cloud engineering stakeholders onsite in a classified environment.

Own and evolve Worldly’s production data lake, Postgres warehouse, CDC and streaming pipelines, dbt transformations, orchestration, BI access controls, and graph integrations. Maintain reliable, governed analytics infrastructure using Iceberg, Trino, Dagster, Terraform, and AWS. Support production generative AI workflows involving embeddings, similarity search, extraction, and classification. Responsibilities include data quality, monitoring, security, incident response, disaster recovery, schema governance, and collaboration with data science and analytics stakeholders.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Designs, builds, and maintains an ontology-grounded enterprise semantic map and metadata graph. Develops automated metadata ingestion, ETL pipelines, API integrations, graph database schemas, lineage and provenance models, and graph queries. Aligns data assets with enterprise ontologies, governance requirements, and mission metadata. Enables semantic search and AI agents, optimizes graph performance, documents architecture, and mentors engineers. Requires production data engineering, graph database, systems integration, and graph query experience, plus an active TS/SCI clearance.

Supports development and maintenance of an ontology-grounded enterprise metadata graph. Responsibilities include automating data-source discovery, ingesting technical metadata, integrating APIs and catalogs, mapping assets to ontologies and provenance layers, maintaining graph databases, supporting lineage and governance metadata, writing basic graph queries, and enabling semantic search and AI-agent workflows. Collaborates with senior engineers and AI teams to connect data assets to enterprise mission workflows.

Design, implement, and maintain secure, scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, security controls, automation, data loading, migrations, integrations, and technical guidance. The consultant collaborates with customers and cross-functional teams throughout implementation and communicates solutions to technical and non-technical stakeholders.

Design and implement secure, scalable geospatial database platforms for defense and intelligence operations. Responsibilities include database administration, performance tuning, backup and recovery, high availability, disaster recovery, access controls, compliance, automation, data integration, and technical guidance. The role also involves collaborating with customers and cross-functional teams throughout requirements, design, implementation, and deployment.

Designs, implements, secures, and optimizes scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, compliance, automation, data loading, migrations, integrations, and customer-facing technical guidance.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

Build and maintain ELT pipelines, data models, semantic layers, and personalization signals for client retention and marketing tests. Partner with machine learning, product, marketing, design, and architecture teams to prepare predictive data, connect signals to CRM channels, support real-time event processing, and document a scalable data foundation. Use AI coding assistants daily to accelerate development, testing, and reviews in a fast-moving discovery environment.

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

Build and maintain reliable ETL and real-time data pipelines, enable data-driven product decisions, ensure relevant event collection, align data roadmap to business goals, and collaborate with platform, security, and analytics teams to deliver actionable insights.

Develops and maintains healthcare data integration and EDI solutions, including ETL processes, data pipelines, data validation, cleansing, transformation, reporting, and compliance testing. Troubleshoots applications, manages code reviews, documents systems, and collaborates with analysts, healthcare providers, business partners, and IT teams. The role also implements security measures and recommends improvements to integration processes.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.