Top Data Engineer Jobs

Design and implement technical solutions with cross-functional teams, produce specifications and documentation, write/test/deploy code, perform and participate in code reviews, support and resolve technical issues, collaborate with QA, and stay current with emerging technologies to incorporate into solutions.

Design, build, and maintain Python-based data applications and scalable data pipelines using AWS Glue and PySpark; deploy containerized services on EKS; implement APIs with API Gateway and Lambda; integrate PostgreSQL, DynamoDB, and Kafka; follow TDD, CI/CD, and document work in Confluence and JIRA while collaborating in Agile teams.

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 and develop AWS-based data platforms, build and maintain ETL pipelines using PySpark, perform data modeling and governance, integrate event-driven systems with Kafka, manage containerized deployments with Docker and Kubernetes, and produce clear documentation while collaborating with stakeholders.

Lead the architecture and deployment of scalable data platforms, real-time and batch pipelines, AI infrastructure data systems, APIs, data lakes, warehouses, streaming architectures, and data quality frameworks. Optimize distributed processing and queries, establish governance, model enterprise data semantics, evaluate modern data technologies, and mentor engineering teams while collaborating across analytics, product, and platform groups.

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.

Lead enterprise-scale data engineering initiatives, including the design, development, deployment, and maintenance of production data pipelines and scalable data platforms. Guide multidisciplinary teams, modernize analytics workflows using Python and SQL, develop shared libraries, and support Palantir Foundry adoption through training, integration, and architecture. Collaborate with stakeholders, provide technical presentations, and apply expertise in data architecture, Databricks, cloud platforms, and distributed data processing.

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.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

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.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Design, build, and maintain scalable AWS-based data pipelines and storage (S3, Redshift, RDS, DynamoDB). Implement IaC (CloudFormation/CDK), ETL optimization, monitoring, security (IAM, OWASP/CIS best practices), and CI/CD/code pipeline controls. Collaborate with product teams, document solutions, and provide training to support analytics and reliable data infrastructure.

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.

Embedded data engineer on the systematic China equity team responsible for end-to-end data lifecycle: sourcing, ingestion, modeling, validation, transformation, and delivery for research and production. Build scalable data pipelines, schemas, quality checks, and monitoring; profile datasets; collaborate with researchers, developers, and vendors; and improve tooling, standards, and vendor relationships to support alpha research and trading.

The Data Engineer will build and maintain scalable data pipelines, ensure data quality, and support a metadata-driven framework while collaborating with stakeholders.

Design, build, and maintain data warehouse infrastructure, semantic/dimensional models, metrics layer, and BI dashboards (Sigma, Power BI, Databricks). Build secure ELT pipelines, enforce governance and row-level security (Entra ID), enable self-service analytics and AI/ML integrations, triage BI issues, train stakeholders, and collaborate in Agile to deliver reliable, performant reporting and curated data products.

Designs scalable data platforms, secure endpoints, ETL systems, and data integration solutions. The role optimizes data processing and performance, implements data governance and security, ensures data quality, creates analytics reports and dashboards, and collaborates with engineers, scientists, reporting teams, and business stakeholders. It also provides technical leadership and develops roadmaps for data analytics projects using cloud, machine learning, automation, and modern data architecture technologies.

Build and maintain scalable data platforms, pipelines, observability metrics, metadata-driven solutions, data transformations, and third-party integrations. Collaborate with data scientists, analysts, DevOps, and business stakeholders to deliver accurate, timely data and meaningful insights. Responsibilities include optimizing workflows, implementing infrastructure as code, and supporting cloud-based data systems.

Build and maintain scalable data platforms, pipelines, observability metrics, metadata-driven solutions, data transformations, and third-party integrations. Collaborate with data scientists, analysts, DevOps teams, and business stakeholders to deliver timely, accurate data and meaningful insights. Responsibilities include optimizing transformations, implementing infrastructure as code, and administering or tuning Snowflake environments.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design, build, and operate scalable data pipelines and platforms for enterprise clients. Develop ETL/ELT workflows, data ingestion and processing for structured and unstructured data, guide cross-functional teams, and support monitoring, scaling, and operationalization of data solutions.