Top Data Engineer Jobs

Lead design and implementation of batch and streaming data pipelines, build data models, implement data quality and testing, manage orchestration and IaC, mentor engineers, and communicate technical direction and tradeoffs to stakeholders.

The Developer, Data and Integration maintains and develops scalable integration solutions focusing on B2B/EDI transactions, working with business units to translate data needs into technical solutions, and ensures the reliability and performance of data flows.

The role involves developing and integrating cloud-native data services and APIs, supporting data transformation, and maintaining backend services using AWS technologies.

Design, build, and operate Alpaca's scalable data platform and lakehouse infrastructure. Build low-latency streaming and CDC ingestion, batch pipelines landing in Iceberg, and tooling for BI and AI consumers. Manage IaC, Kubernetes deployments, monitoring/incident response, and partner with DevOps and analytics teams to meet growing data requirements.

Design, build, and optimize scalable data pipelines and models using Snowflake, SQL, Python, Kafka, dbt, and Argo. Support near-real-time integrations, CI/CD workflows, data quality, and performance; mentor engineers; partner with product and analysts to deliver production-ready, AI-enabled data solutions.

Senior Data Engineers design and maintain data pipelines, leveraging cloud technologies to process large datasets and support data-driven decisions.

Design and implement modern data pipelines using cloud technologies. Lead and collaborate with teams, ensuring solutions meet customer needs while mentoring engineers.

As a Senior Data Engineer, you'll design and implement data pipelines using cloud technologies, ensuring optimal solutions for data management. You'll collaborate with stakeholders to meet technical needs and stay updated on industry trends.

Design, implement, and maintain data pipelines on cloud platforms, provide technical leadership, and ensure solutions align with customer needs.

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 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 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.