Top Data Engineer Jobs

Manage and maintain PostgreSQL databases, ensuring availability, performance, and security. Oversee disaster recovery processes, user management, and database optimization. Collaborate to streamline operations using AI tools and support development teams with best practices.

The Data Engineer will design data models, build data warehouses and lakes, automate data pipelines, and enable data accessibility while maintaining security standards in a complex environment.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Design, build, and maintain scalable ETL/ELT pipelines and lakehouse data platforms on Azure and Databricks. Implement Medallion architecture and Delta Lake, enforce data governance and quality, optimize pipeline performance, and support CI/CD and environment promotion to deliver reliable financial reporting and analytics data products.

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.

Design and maintain data collection infrastructure and resilient pipelines, troubleshoot and resolve data defects, implement maintenance strategies, support BI/warehousing/reporting, build reports and visualizations, and collect/extract/clean data end-to-end, including real-time streaming and performance tuning.

The Data Engineer II will build and optimize data pipelines, develop reusable data sets, and ensure data integrity and security.

As a Data Engineer, you'll transform raw data into refined offerings, collaborate with stakeholders, build central logic layers, and leverage AI tools for efficient data workflows.

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.

Expand and optimize data pipelines for cross-functional teams, support developers and analysts, implement process improvements, and build analytical tools for business insights.

Design and build high-volume, low-latency data solutions: ingest and normalize diverse data sources, implement ETL and streaming pipelines, build CI/CD for data software, and maintain observability and monitoring.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Lead analytics engineering initiatives, design data pipelines, develop business intelligence solutions, and ensure data governance while collaborating with stakeholders.

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 help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

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.

The Data Engineer will design and build scalable data systems, contribute to the architecture of the data platform, and collaborate with AI/ML engineers to enhance automation and intelligence in Canoe's systems.

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Lead design, build, and operate scalable AWS data solutions (ingestion, batch/streaming, transformation, modeling, warehouses/lakes). Collaborate with product and analytics teams, enforce data governance, debug production issues, and drive best practices, automation, and operational excellence.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion-layer data products. Integrate enterprise applications (including DataStage and ServiceNow), implement data quality, governance, and operational support, and collaborate with cross-functional teams to deliver scalable, production-ready data assets for analytics, reporting, and AI.