Top Data Engineer Jobs

Design, build, and maintain scalable data pipelines and platform infrastructure. Define data governance, monitoring, and quality standards. Partner cross-functionally with Product, Engineering, Analytics, and Data Science. Drive technical decisions, performance optimization, and infrastructure-as-code for a growing data platform supporting patients, providers, and business teams.

Design, develop, and secure database systems that integrate structured and unstructured data from diverse sources. Build ETL processes, SQL/PLSQL/T-SQL routines, Oracle APEX web apps, and Power BI reporting to enable data mining, quality checks, and analytics for engineering and materials data.

Lead design and implementation of AWS GovCloud data lakehouse solutions and scalable ETL/ELT pipelines to migrate legacy warehouses into ontology-ready data products for Palantir Foundry. Provide technical leadership, define data models, metadata/tagging, security controls, SLAs, data quality, monitoring, and CI/CD for data pipelines. Collaborate cross-functionally to enable analytics, digital thread, and governed self-service data across Boeing BDS.

Design, implement, and operate data quality and governance solutions using Anomalo, Atlan, Unity Catalog and Databricks. Build automated data quality checks, validation rules, connectors, prototypes and dashboards with Python, SQL and Streamlit. Collaborate with data stewards, product and engineering teams to define CDEs, prioritize remediation, document standards, drive tool adoption, and support Agile delivery using Jira.

The Senior Data Engineer designs and maintains data systems, develops data pipelines for ingestion and analysis, and optimizes data architecture to support business needs.

The role involves gathering requirements for BI and data projects, providing documentation and support, and analyzing data to facilitate decision making and system improvements. The candidate will liaise between business units and IT, support application deployments, and drive transformation projects.

The Data Engineer will design and maintain ETL/ELT pipelines, optimize data storage, develop CI/CD pipelines, and ensure data quality and governance for federal clients.

As a Data Engineer, you will design and build scalable data pipelines and models, maintain data governance, and collaborate with AI engineers while ensuring data quality and compliance.

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

The Customer Data Engineer will design, maintain, and optimize data models for the Customer Data Platform, ensuring high-quality data integration and analytics. Responsibilities include building SQL queries, managing data structures, and supporting analytics with a focus on data quality.

As a Data Engineer II, you will build and operationalize complex data solutions, ensuring data quality and governance while supporting AI and business intelligence applications.

The Data Engineer will design, develop, and maintain data infrastructure, focusing on ETL processes, Apache Spark, Python programming, and CI/CD implementation to ensure efficient data flow and processing.

The Data Engineer designs and maintains scalable data management systems, implements data governance, and builds ETL workflows to support analytics and reporting needs.

Build and deliver end-to-end, production-grade data solutions: design and maintain scalable ETL pipelines, ingest from diverse sources, implement dbt transformations, ensure data quality and observability, optimize performance and cost, and partner with analytics, product, and engineering teams to drive business impact.

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 Data Governance Engineer focuses on unstructured data governance, metadata cataloging, data quality management, and collaboration with stakeholders to enhance data compliance and discoverability.

The Data Governance Engineer advances data governance initiatives, focuses on metadata cataloging, lineage harvesting, and governance workflows, and collaborates on data quality and compliance.

Lead architecture and delivery of SoFi's data platform: build scalable data pipelines, drive cloud-native and AI-enabled tooling, enforce governance and security, mentor senior engineers, and support production on-call.

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

As a Lead Data Engineer, lead development in actionable technical solutions within Agile teams, utilizing expertise in programming, big data tech, and cloud-based data services.

The Senior Lead Data Engineer will drive major technological transformations, lead development teams, and design innovative data solutions while utilizing cloud services and big data technologies.

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.

Design, build, test, and support ETL/ELT data pipelines and data warehouse solutions. Integrate and cleanse data via APIs/webhooks, perform data profiling and modeling, enforce governance, maintain code in Git, troubleshoot pipeline issues, document systems, and collaborate with business and data teams.

Design, build, and maintain scalable batch and streaming data pipelines and data models using Python/PySpark, Snowflake, and cloud platforms. Optimize performance, implement orchestration (Airflow), enforce data governance/metadata standards, create data quality frameworks, support CI/CD and DevOps, monitor production pipelines, and collaborate with stakeholders to evolve the enterprise data platform.

Design, build, and maintain scalable data pipelines and AI-powered solutions using Python, Spark, Scala, Kafka, Databricks, and cloud data services. Focus on KTLO activities including vulnerability remediation, tool/version upgrades, data security, platform stability, performance tuning, and automation. Collaborate with cross-functional teams to model data, optimize queries, and support streaming and workflow orchestration (Airflow).