Top Data Engineer Jobs

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.

Lead design and implementation of enterprise data platforms, real-time pipelines, feature stores, and agentic data infrastructure. Ensure data governance, quality, lineage, and reliability while partnering with scientists, engineers, and product leaders. Own technical roadmap, mentor engineers, and drive platform scalability and engineering best practices.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

Design, develop, and maintain scalable ETL pipelines using Ab Initio to transform upstream data for Oracle Financials SaaS. Perform data profiling, quality assessments, complex SQL development, Unix/Linux scripting, metadata management in EME, deployment support, performance tuning, and collaborate with architects and analysts. Provide operational support and mentor junior analysts while ensuring compliance and risk controls.

Design and optimize enterprise-scale data pipelines in Microsoft Fabric, migrate on-prem relational data to OneLake, build streaming ingestion, implement lakehouse/warehouse patterns, ensure performance and governance, support RAG/vector search workflows, maintain Power BI reporting, drive CI/CD, and mentor junior engineers.

The Senior Principal Data Analytics Engineer will lead transformations in data architecture and analytics, focusing on high-quality, accessible data for decision-making, while collaborating with teams, ensuring governance, and optimizing performance.

As a Senior Lead Data Engineer at Capital One, you'll lead a development team, utilizing various big data and cloud technologies to deliver impactful solutions. Responsibilities include collaborating with Agile teams, mentoring, and ensuring optimal code performance.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Own and scale the data platform powering ReadyOn's real-time labor OS. Design and operate batch and real-time pipelines, transformations, and data services (Python, PySpark, Airflow, Glue, Snowflake, Postgres). Build connectors, observability, data quality, and feature-store infrastructure while partnering with AI, backend, and product teams and providing technical leadership and mentorship.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, build, and maintain scalable ETL pipelines, data warehouses, and analytics infrastructure integrating EMR, revenue cycle, and operational data. Ensure data quality, HIPAA compliance, support reporting/BI, enable analytics, and collaborate with clinical, finance, and IT teams.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Lead technical design and delivery of enterprise data warehouse and BI solutions. Own data architecture, complex ETL/ELT development, testing, performance tuning, and prototyping. Mentor teams, coordinate cross-functional resources and vendors, enforce best practices, and ensure data readiness and quality to enable analytics and business value.

Design, build, and maintain cloud-based data pipelines and products to support analytics and ML. Create discoverable datasets, collaborate with embedded teams to capture system metrics, perform aggregation and visualization, develop production-ready Python/SQL/PySpark workflows, and implement monitoring, dashboards, and alerting to ensure data quality and system health.

Lead enablement and coordination across domain teams to establish enterprise cloud data standards, platform onboarding, and data warehouse integration. Drive adoption of data engineering frameworks, reusable components, governance, data quality, stakeholder alignment, and continuous improvement to scale platform delivery across multiple teams.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, develop, test, implement, and support cloud-based data solutions and pipelines. Collaborate across Agile teams on distributed systems, microservices, ML integration, streaming, NoSQL/RDBMS, and data warehousing (Redshift/Snowflake). Mentor engineers, perform unit tests and code reviews, and optimize performance.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Design, build, and scale resilient data pipelines and infrastructure on AWS. Own the data ecosystem using Databricks or Snowflake, write production-quality Python and SQL code, collaborate with cross-functional teams, and prepare data for analysis and product use in a fast-moving startup environment.

Design and implement scalable data ingestion, ETL/ELT pipelines and transformations into Microsoft Fabric using ADF and notebooks (Python/Pandas/Spark). Build and maintain dimensional data warehouse models, ensure data quality, performance tuning, and prepare data for semantic models. Collaborate with stakeholders, participate in code reviews, documentation, and operational handoffs while establishing standards for deployment and governance.

As a Data Engineer, you will design and build robust data pipelines, manage data infrastructure, and support data-driven strategies using advanced engineering techniques.