Top Data Engineer Jobs

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.

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.

Lead architect and hands‑on senior data engineer who designs scalable data platforms and medallion architectures, optimizes ETL/ELT and data modeling, implements CI/CD and IaC, enforces data governance and security, mentors engineers, integrates BI tools, and leads client-facing technical engagements.

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.

Provide production support for SQL Server databases, ETL/data integration, Azure cloud services, and enterprise reporting. Monitor performance, troubleshoot issues across Windows and Linux servers, support file transfers and enterprise applications, and collaborate with technical teams to ensure data availability and reliability.

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

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.

Build and maintain ETL/ELT data pipelines and storage for batch and real-time use. Partner with product and engineering to define requirements, ensure data quality, monitor pipeline health, integrate cloud data warehouses/lakes, optimize SQL/Python transformations, and document data flows and architecture.

Build and maintain scalable GCP-based data pipelines, ETL/ELT processes, and data models. Support migrations from legacy systems, enable ML integrations, enforce data quality and lineage, document Terraform modules, implement CI/CD across environments, and provide production support and performance tuning within Agile teams.

Designs, develops, and optimizes enterprise data pipelines, ETL processes, data models, databases, and healthcare analytics solutions. Supports database administration, performance tuning, security, high availability, disaster recovery, deployments, and data governance. Collaborates with analysts, data scientists, application developers, and stakeholders to deliver reliable data infrastructure for AI, machine learning, business intelligence, and population health initiatives.

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.

Design and lead scalable ETL pipelines and data integration solutions, consolidate varied sources into centralized data warehouses, optimize SQL and Python data processing, mentor engineers, and collaborate with cross-functional teams to deliver compliant, high-performance data products and analytics capability.

Design, build, and operate enterprise-grade, cloud-native data platforms and scalable ETL/ELT pipelines for analytics and ML. Collaborate with cross-functional teams to implement data models, ensure performance, security, governance, and support production data solutions in Agile environments.

Design, execute, and support end-to-end data migration into SAP S/4HANA: extraction, transformation, validation, reconciliation, and cutover. Use LTMC/SLT/LVM and SQL to migrate and validate master and transactional data, collaborate with functional teams, troubleshoot issues, and document migration processes.

Design, build, test, and maintain ETL/ELT data integration pipelines and data architecture. Perform data mapping, profiling, quality remediation, and support BI/analytics by partnering with business and technical teams. Provide full lifecycle support, documentation, and continuous improvement in a hybrid agile environment.