Top Data Engineer Jobs

Build, operate, and optimize data platforms and pipelines (Snowflake, Azure ESB). Manage ETL, data migrations, ingestion, quality, and analytics tools (Cognos, Tableau). Collaborate with IT and business stakeholders, enforce data governance, support regulatory and cybersecurity requirements, and provide advanced support and system integration for banking APIs.

The Data Engineer will develop and maintain data pipelines, perform backend integrations, and enhance data systems for effective reporting and analytics.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Build and improve data pipelines, integrations, and AI agents for Starlink Enterprise. Clean and enrich CRM and go-to-market data, develop MCP servers and connectors, instrument data and agent performance, and partner with sales and ops to automate workflows and reduce manual cleanup.

Improve go-to-market data quality and build integrations and AI agents for Starlink Enterprise. Implement validation, enrichment, deduplication, MCP interfaces, and automations across CRM and operational systems. Instrument data and agent performance, partner with sales and ops, and ship reusable tools and monitoring to accelerate lead-to-revenue workflows.

Design, build, and optimize scalable data pipelines, ETL/ELT processes, and data architectures. Develop dimensional models and semantic layers, ensure data quality and integrity, maintain data warehouses and lakes, collaborate with stakeholders to translate requirements, document solutions, and support analytics using platforms like Databricks, Snowflake, DOMO, and Fabric.

Own and scale end-to-end data infrastructure: build production ETL pipelines, design scalable schemas, enforce data quality/governance/security, create self-serve data models for analytics and ML, instrument observability, and partner cross-functionally while participating in on-call incident response.

Design, build, and maintain scalable Hadoop-based data processing pipelines and ETL for structured and unstructured data. Implement streaming and batch solutions (Spark, Storm, Kafka), integrate multiple data sources and NoSQL stores, optimize performance, define retention, and collaborate with product and data science teams in an Agile environment.

Design, implement, and own reliable data solutions across design, manufacturing, and mission operations. Lead data architecture, migrations, ETL/replication, schema design, and optimizations. Partner with cross-functional teams, drive tradeoffs, prioritize work using data, mentor team members, and deliver production-ready data products.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design and implement knowledge-graph solutions for intelligence customers: model graph data, develop queries and integrations, work with software teams in classified environments, and communicate analytic concepts to stakeholders.

The Data Engineer will design and implement data architecture, develop data ingestion pipelines, collaborate with stakeholders, and improve data-related processes.

Lead end-to-end migration of document-heavy legacy systems to cloud, converting unstructured records into analytics-ready formats, ensuring data quality and governance. Build and maintain MuleSoft integrations and APIs, apply data transformation (DataWeave), support Salesforce Data 360 implementation, and ensure lineage, metadata, and compliance while collaborating in Agile teams for phased cutovers and audit readiness.

Design, build, and maintain scalable cloud-native data platforms and pipelines (batch, streaming, near-real-time). Implement distributed processing (Spark), event-driven systems (Kafka), governance, security, observability, and support ML/analytics. Partner with cross-functional teams, optimize performance/cost, and mentor engineers while enabling production-grade data products.

The AI Data Engineer will build data pipelines and semantic models, implementing Snowflake and dbt for AI products, focusing on data accuracy and usability for LLM tools.

Design, build, and maintain scalable, real-time data pipelines into the data lake/warehouse. Ensure data quality via audits, troubleshoot large-scale workflows, optimize queries, collaborate with cross-functional teams, and support data-driven decision-making.

Build, test, and maintain ETL pipelines and metadata structures to prepare data for analytics and data science. Perform data profiling, mapping, transformations, data quality rules, and validate results. Design and support Spotfire dashboards and provide post-deployment maintenance and stakeholder support.

Design, develop, and maintain scalable data platforms and pipelines. Build ETL/ELT workflows to ingest, parse, and process structured and unstructured data. Support scaling, monitoring, and operation of data stores, collaborate with cross-functional teams, and apply data engineering best practices for mission-driven projects.

Design, build, and maintain scalable data pipelines, warehouses, and lakes. Develop ETL/ELT processes, ensure data quality, governance, and security, and collaborate with data scientists while monitoring platform performance and troubleshooting workflows.

Build and maintain end-to-end data pipelines, perform ETL and data integration, troubleshoot data processing errors, implement and maintain data models and warehouses, document data flows, collaborate with cross-functional teams, support BI initiatives, and improve data governance and platform practices.