Top Data Engineer Jobs

Develop and maintain Python data pipelines and CI/CD deployments to build business data objects. Implement source-to-target mappings, unit and integration tests, and resolve complex technical issues. Follow SDLC and software product design practices, promote software quality, and collaborate across functions while working onsite in Chicago or Peoria.

Design and implement data pipelines, manage data with SQL and Python, automate workflows, ensure data integrity, and build scalable data solutions on AWS and Redshift.

As a Data Engineer, you'll design, build, and maintain ETL/ELT pipelines, develop data infrastructure, collaborate with BI teams, and optimize data models for reporting.

Build and maintain batch ETL/ELT pipelines and Airflow orchestration using Python, SQL, and AWS. Develop operational datasets for fulfillment, inventory, shipments, and returns. Monitor and optimize pipelines, implement data quality checks and alerting, support production incident response, and collaborate with engineering, BI, and operations teams to improve pipeline scalability, observability, and reliability.

Design and implement ETL pipelines, optimize data systems, build data warehouses, and develop AI/ML models for data solutions. Collaborate with teams to deliver data-driven products and ensure data quality and governance.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

The Data Engineer will architect ETL pipelines, manage a central data lake, and ensure data quality and best practices in collaboration with various teams.

The Senior Data Engineer will design and execute data management strategies, build cloud solutions, develop data pipelines, and ensure data quality and integrity.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

Design and implement large-scale analytics data structures and ingestion pipelines (real-time and batch). Build ETL/ELT processes, participate in technical decisions, review code, and document architecture and solutions for analytics users.

The Principal Data Engineer will lead the technical strategy for data initiatives, focusing on real-time data platforms and collaboration with stakeholders to design efficient and innovative data architectures, ensuring data governance and compliance.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and operate scalable data systems for People Analytics, ensuring reliability and privacy of sensitive employee data. Collaborate with leadership to create data products for decision-making.

The Data Engineer will support data initiatives, build data pipelines, perform statistical analyses, and develop BI dashboards to drive business decisions.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Entry-level Data Engineer responsible for building and maintaining cloud-based data assets and pipelines, improving data quality and automation, investigating data integrity issues, and collaborating with architects, analysts, and stakeholders. Uses SQL and Python, participates in Agile practices, leverages AI-powered developer tools for spec-driven development, and follows responsible AI and data privacy practices.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Own and operate the end-to-end consumer data pipeline: ingest, clean, transform, and unify multi-source behavioral data into a deduplicated identity graph. Build and run batch and streaming pipelines, enforce data-quality and observability, support low-latency AI query access, perform root-cause incident analysis, and ensure privacy, compliance, and clear data lineage. Collaborate across product and fulfillment teams to maintain SLAs and document data contracts and architecture.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.