Top Data Engineer Jobs

Design, build, and maintain data architectures, ETL pipelines, and data models to enable analytics across Marketing, Finance, Sales, and Logistics. Implement integrations from cloud sources, secure API connections, perform large-scale data mining and entity resolution, translate algorithms into code, and use SSIS and BI tools to deliver reliable reporting and dashboards.

Design, build and scale a 500+ TB Audience Management Big Data platform. Own data ingestion, validation, transformation and storage pipelines. Write server-side Spark/Scala code, perform R&D on new technologies, mentor engineers, and partner with product managers to meet roadmap and business needs.

Lead design and implement scalable data infrastructure and transformations. Build data repositories, convert data science algorithms into robust pipelines, mentor a small data engineering team, ensure delivery quality, and collaborate with Product, Data Analysts, and Data Scientists.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.

Design, build, and maintain scalable data products and data warehouses to measure content performance and subscriber movement. Develop ETL pipelines in cloud and big-data environments, define data models, implement automated data quality checks, tune SQL performance, document solutions, meet SLAs, and collaborate with product, analytics, and infrastructure teams using agile practices.

Design, build, and maintain scalable, fault-tolerant identity and device data pipelines and backend systems. Modernize data models, improve code quality and test coverage, support production operations and on-call, collaborate with product and cross-functional teams, and participate in code reviews and agile ceremonies.

Design, build, and maintain Identity Householding data products and scalable batch/real-time pipelines. Improve data models and code quality, increase test coverage, collaborate with product and engineering teams, participate in on-call support, code reviews, and CI/CD-driven deployments using Databricks, Spark, cloud databases, Airflow, and AWS S3.

The Sr. Data Engineer will develop data pipelines, automate data processing, collaborate in Agile teams, and ensure data extraction reliability while adhering to engineering principles.

The Sr. Data Engineer will manage data pipelines, develop ETL processes, support continuous automation, ensure data reliability, and work collaboratively in Agile teams to meet client needs.

The Senior Data Engineer will oversee data operations, design scalable data pipelines, ensure data quality, and support cross-functional data initiatives.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

The role requires designing, building, and maintaining data pipelines using Big Data technologies. Responsibilities include developing ETL processes, ensuring data integrity, collaborating with teams, and applying AI techniques to drive efficiency.

The Data Engineer will design and maintain data pipelines using Big Data technologies, optimize ETL processes, and apply AI techniques. Responsibilities include collaborating on data solutions and ensuring data quality and security.

Design, build, and operate a privacy-safe, cloud-based data platform supporting batch and real-time pipelines, analytics, ML/AI, governance, observability, and high pipeline reliability. Partner with stakeholders, set data engineering standards, troubleshoot production incidents, and mentor engineers.

Lead data architecture and hands-on engineering to build a cloud data warehouse on Snowflake using Data Vault 2.0 and dbt. Design standards, implement ETL/ELT pipelines with Python and SQL, ensure data quality and governance, mentor engineers, and support on-prem to cloud migration.

Design, build, and maintain scalable data pipelines, warehouse architecture, and BI solutions. Lead data modeling, integration (CDC/streaming), infrastructure reliability, and governance. Collaborate with data scientists, analysts, and partners, enforce PHI security, mentor teammates, and support on-call and evolving BI technologies.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

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.

As a Data Engineer at MAI, you will develop and maintain data platforms, design ETL/ELT processes, ensure data quality, and collaborate with teams to optimize data utilization for analytics and ML applications.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

The Senior Data Engineer will design and maintain data architecture and integration solutions, develop enterprise data pipelines, enforce data quality, and mentor junior team members while collaborating with business stakeholders.

The Senior Data Engineer will develop and optimize software applications and data platforms for NAVOCEANO, supporting mission-critical solutions, data pipelines, and analytics capabilities.

Lead design and implementation of large-scale data pipelines, ETL, and warehousing. Build and manage databases, ensure data quality/security, collaborate with analysts and scientists, evaluate new technologies, mentor junior engineers, and manage project delivery.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.

Design, build, and maintain ETL/ELT data pipelines and domain-oriented data products to support a Digital Thread. Ensure data quality, governance, mapping, and discoverability. Apply software-engineering practices (version control, testing, CI/CD, IaC) for production deployments. Integrate manufacturing and operational systems and prepare AI/ML-ready datasets for analytics and predictive use cases.