Top Data Engineer Jobs

The Senior Data Engineer will design, implement, and maintain cloud-based data pipelines, providing technical leadership and ensuring systems meet business requirements while staying current with technology trends.

The role involves designing and implementing data pipelines, advising clients on data engineering solutions, and mentoring engineers in a collaborative environment.

Design and maintain data pipelines, provide technical leadership, and collaborate with teams to deliver cloud-based data solutions for clients.

The Senior Data Engineer will design and maintain data pipelines, provide technical leadership, and work with cloud platforms like AWS, Azure, and GCP.

Design and maintain data pipelines using cloud technologies. Provide technical leadership, implement data engineering solutions, and mentor engineers. Collaborate with stakeholders and stay updated on cloud advancements.

The Senior Data Engineer will design and maintain data pipelines, provide technical leadership, collaborate with teams, and ensure optimal solutions using cloud technologies.

Design, build, and maintain data pipelines and models for hardware manufacturing, device traceability, yield, and supply chain. Integrate MES/ERP/IoT data into a lakehouse, ensure end-to-end data quality, security, and governance, and partner with Hardware, Operations, and Quality teams. Support lakehouse architecture (Iceberg, Databricks, dbt) and reduce single-point-of-failure risk for manufacturing data.

The Sr. Data Engineer will build and manage data pipelines for large datasets, collaborate with teams, and leverage cloud technologies for data processing.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Design and build scalable data lakes, warehouses, and lakehouses; implement Python ETL/ELT pipelines; orchestrate workflows with Airflow; ingest data from third-party APIs; optimize Parquet/Avro/ORC storage; work with Spark, Snowflake, and AWS/GCP; consult stakeholders and build infrastructure with Terraform.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest from 3rd-party APIs, optimize columnar file formats, support ML initiatives, and consult with stakeholders to translate business goals into data architecture roadmaps using Snowflake, Spark, and cloud infrastructure.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines, orchestrate with Airflow, ingest from third-party APIs, optimize columnar formats and query performance, work with Spark, Snowflake and cloud (AWS/GCP), and consult stakeholders to translate business goals into data architecture roadmaps.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar file formats and SQL, support ML initiatives, consult stakeholders, and provision AWS/on-prem infrastructure using Terraform.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines and Airflow orchestration, ingest from third-party APIs, optimize columnar formats (Parquet/Avro/ORC), and work with Snowflake, Spark, and cloud (AWS/GCP). Serve as technical consultant translating business goals into data architecture and use Terraform for infrastructure.

Design and build scalable data lakes, warehouses, and lakehouse architectures; implement Python ETL/ELT pipelines and Airflow orchestration; ingest data from third-party APIs; manage columnar file formats (Parquet/Avro/ORC); optimize pipelines for Snowflake, Spark, and AWS; consult stakeholders to translate business goals into technical roadmaps; use Terraform for infrastructure.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest from third-party APIs, optimize file formats and columnar storage, work with Spark/Snowflake and cloud (AWS/GCP), support ML initiatives, consult with stakeholders, and provision infrastructure via Terraform.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest from third-party APIs, optimize columnar formats, support ML/AI initiatives, and consult with stakeholders to translate business needs into technical roadmaps using cloud and infrastructure tools.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar file formats and SQL, support ML initiatives, consult with stakeholders to translate business requirements into data architecture roadmaps, and build AWS/on-prem infrastructure using Terraform and related tools.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar storage and query performance, and consult stakeholders to translate business goals into data architecture roadmaps using Spark, Snowflake, and cloud infrastructure.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines and Airflow orchestration. Build ingestion workflows from third-party APIs, optimize performance using Spark and Snowflake on AWS/GCP, and consult with stakeholders to translate business goals into data architecture roadmaps.

Design, build, and optimize scalable, secure data pipelines and cloud data warehouse solutions. Ensure data quality, governance, and cross-domain data models; mentor engineers, support CI/CD/DevOps, and enable analytics/AI use cases.

Develops, documents, and maintains data engineering processes supporting big data, data lake, and lakehouse infrastructure. Collaborates with analytics teams and business stakeholders to implement data solutions, troubleshoot complex platform issues, tune code, review architecture and testing, and protect data integrity. The role also mentors junior team members, supports cross-functional projects, and ensures compliance with organizational standards and policies.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

Design, build, and operate a scalable, secure data platform. Architect end-to-end data pipelines, manage Snowflake performance and costs, implement CI/CD and observability, ensure data governance and HIPAA alignment, integrate diverse sources, mentor teammates, and support analytics and business users.