Top Data Engineer Jobs

The AI Data Engineer will build data pipelines and semantic models, implementing Snowflake and dbt for AI products, focusing on data accuracy and usability for LLM tools.

The Data Engineer designs and maintains data pipelines and database infrastructure, ensuring reliability, optimizing data models, and supporting analytics across the organization.

Responsible for designing, building, and maintaining scalable data pipelines and transformation processes, ensuring data quality and collaborating with teams.

Design and deliver scalable data platforms and pipelines, enable data science teams, and implement best practices in automation and governance.

As a Data Engineer, you will design and maintain data infrastructure, build ingestion pipelines, and collaborate with ML and product teams for data-driven features.

The role involves designing and maintaining data pipelines, optimizing data architecture, developing visual analytics with Tableau and Power BI, project management, and collaboration with cybersecurity teams.

The Looker Data Engineer will build and optimize data models, develop reporting solutions, and support data-driven decisions using Looker and other technologies.

The Sr. Data Engineer is responsible for application development, database performance monitoring, data analytics, and ensuring compliance with quality standards.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

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

As a Data Engineer at Booz Allen, you'll manage data engineering activities, develop scalable platforms, and work with multidisciplinary teams to extract meaningful insights from big data.

Design, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

The Data Engineer IV is responsible for designing and building data pipelines, implementing transformation logic, ensuring data quality, optimizing data infrastructure, and collaborating with stakeholders. This role requires advanced skills in data engineering, management of projects, and mentoring of junior developers.

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.

Lead design, development, and implementation of data management, ETL, data pipelines, reporting, API integrations, and automation for Head Start programs. Provide technical leadership, project management, data governance, system performance oversight, stakeholder coordination, and continuous improvement to deliver scalable, secure solutions.

Design, build, and maintain scalable Azure data pipelines and platforms using Azure Databricks, Synapse, Fabric, and Data Factory. Implement IaC (Bicep/ARM/Terraform), CI/CD with Azure DevOps, optimize Spark jobs, monitor platform stability, and collaborate with architects, BI developers, and clients to meet Azure best practices and business requirements.

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.

Design, implement, and maintain scalable data pipelines and platforms for structured and unstructured data to support AI/ML workloads. Ensure data governance, security, monitoring, and CI/CD for ETL workflows across on‑prem, cloud, or hybrid environments while collaborating with cross‑functional technical teams.

Lead technical strategy and roadmap for an enterprise Databricks-based data platform. Drive governance, metadata, lineage, and semantic layer standards; build reusable data products; enable self-service analytics and scalable ML operations; mentor engineers and align platform investments with business outcomes.

Own and operate the unified data platform and data lake powering fraud detection. Design, build, and run low-latency batch and streaming ingestion pipelines, ensure data quality/lineage/observability, support embedding/vector pipelines for generative AI, and manage deployment and CI/CD on Kubernetes while partnering with data science and product teams.

Lead Data Engineer builds and maintains foundational, cloud-based data platforms for transaction analytics. Collaborates with Agile teams to design, develop, test, and deploy data solutions using big data, streaming, and warehousing technologies, mentors engineers, and ensures performant, production-ready code.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

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.

The Senior Lead Data Engineer will design, develop, and support technical solutions, lead a team, and work with cloud-based services, focusing on data integration and transformation.

As a Lead Data Engineer, you'll design and build scalable data platforms, develop streaming data pipelines, and collaborate within Agile teams to implement data solutions.