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.

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.

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.

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.

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.

Design, build, and operate scalable ETL/ELT pipelines and data platform capabilities to support analytics, AI/ML, and product needs. Lead technical designs, mentor engineers, implement DataOps/CI/CD and observability, ensure data quality/governance/security, and optimize performance and cost across cloud-based data systems.

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 initiatives, consult with stakeholders, and provision AWS/on-prem infrastructure with Terraform.

Designs, builds, and maintains scalable ETL/ELT pipelines, data workflows, integrations, and curated Silver and Gold data assets. Supports analytics, reporting, machine learning, and operational applications through batch and streaming processing. Responsibilities include data ingestion, transformation, validation, quality management, automation, documentation, and performance optimization across enterprise data platforms. Partners with cross-functional teams, modernizes data processes, and mentors junior engineers.

Design and own Ceresti’s end-to-end data architecture, including secure raw-data storage, validated file and API ingestion, Postgres pipelines, and curated analytics layers. Establish governance for HIPAA-protected data, data quality, lineage, observability, and access controls. Build ML feature tables and embedding capabilities, collaborate on data contracts, support Agile delivery, and mentor engineers on SQL and data-system design.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and cloud-based data platforms. Optimize systems for performance, reliability, security, and cost while implementing monitoring, governance, and data quality practices. Collaborate cross-functionally with data scientists, analysts, engineering, and product teams to deliver reliable data solutions and improve the data ecosystem roadmap.

Build and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and data platform tools. Develop cloud-based infrastructure using AWS and Terraform, optimize processing for reliability and cost, and implement monitoring, logging, governance, quality, and security practices. Collaborate with data scientists, analysts, autonomy engineers, and product teams to deliver reliable data solutions.

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 file formats, support ML initiatives, and consult stakeholders to translate business goals into data architecture roadmaps.

Design and develop scalable data solutions and tooling to enable AI and advanced analytics across Azure hardware and software stacks. Identify opportunities for AI-driven impact, build data platforms and visualizations, and collaborate with engineers, data scientists, and program managers to improve efficiency, quality, and operational insights.