Top Data Engineer Jobs

Build and maintain greenfield data infrastructure and pipelines (ingest, validate, organize) for basketball and performance datasets. Implement medallion architectures, data validation, and optimizations; collaborate with cross-functional stakeholders to deliver analytic datasets for web and mobile applications.

Designs and maintains batch and streaming data pipelines using Databricks, AWS, Python or Scala, SQL, and orchestration tools. The role covers data quality, governance, cataloging, lineage, API integration, and regulated pharma data environments. Responsibilities include translating business needs into technical specifications, partnering across pharma functions, presenting to executives, prioritizing data initiatives, supporting adoption, and building new data domains. The engineer also applies warehousing, dimensional modeling, documentation, and stakeholder coordination practices.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Operate and evolve AMP platform to ensure reliability, security, compliance, cost efficiency, and operational excellence. Monitor health and capacity, manage incidents and escalations, provision Azure/Fabric resources, enable onboarding, drive security and compliance remediation, optimize costs and capacity, coordinate with product groups and vendors, and improve automation, observability, and operational tooling.

Own and operate the iMData platform: design and build Databricks-based ETL pipelines using PySpark and medallion architecture, create gold-layer star schemas, migrate assets from Synapse to Databricks, implement CI/CD and Terraform IaC, ensure data security and observability, mentor junior engineers, and collaborate with stakeholders and ML engineers to deliver production-grade analytics and AI-ready data.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Designs, develops, and maintains scalable data pipelines and workflows using Matillion, HVR, and Databricks. Performs data modeling, SQL-based transformation, ETL/ELT optimization, and data quality management. Collaborates with data scientists, analysts, architects, and business data owners to translate requirements into secure, governed data solutions. Documents processes and ensures compliance with security and regulatory standards, including U.S. government cloud requirements.

Build and maintain scalable data pipelines and architectures for AI and machine learning use cases. Prepare structured and unstructured datasets for training, fine-tuning, and inference while ensuring data quality, reliability, and governance. Collaborate with ML and AI teams to support data requirements and solve complex system-design challenges using technologies such as Airflow, dbt, Spark, data warehouses, and streaming platforms.

Owns the data backbone for CISA’s infrastructure mapping platform by acquiring, validating, integrating, governing, and documenting large geospatial and telecommunications datasets. Designs schemas, architectures, data dictionaries, and management plans; builds repeatable ETL and quality assurance pipelines; maintains ESRI enterprise geodatabases and ArcGIS Enterprise data; supports emergency and national security events; and prepares datasets for government transition or handover.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Own end-to-end data work: build and maintain dbt models and pipelines, ensure warehouse data quality, design Tableau dashboards, automate recurring reports, partner with stakeholders to translate needs into analytical products, and leverage Python and AI tools to improve data infrastructure and analytics.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

The Cloud Data Engineer will design, implement, and manage data solutions on Microsoft Azure, create and maintain data pipelines, and ensure data security for decision-making processes.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

Build and maintain scalable batch and streaming data pipelines for large and semi/unstructured data to support reporting, data science, ML training/execution and search analytics. Evaluate and deploy data technologies, collaborate with architects, data scientists and business partners, and handle coding, testing, deployment, monitoring, documentation and automation in an Agile environment.