Top Senior Data Engineer Jobs

The Data Engineer will design and build scalable data pipelines, optimize data infrastructure, and collaborate with teams for data solutions in an eCommerce environment.

The Principal Data Engineer leads the development of scalable data solutions, collaborates on product roadmaps, influences organizational change, and coaches junior staff in a hybrid work environment.

The Data Engineer, Senior will design and maintain data systems, build ETL pipelines, and collaborate with a team to optimize data solutions for client missions.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, build, and maintain ETL pipelines moving data from a shared file transfer service into S3 and Snowflake. Ensure data quality, monitor pipelines, troubleshoot failures, and collaborate with business and analytics teams to meet downstream reporting and application needs.

Design, build, and maintain data structures, ETL/ELT pipelines, and data products. Support operational data processes, performance tuning, data governance, documentation, and stakeholder collaboration. Mentor junior staff and improve data platform quality and accessibility.

Design and build foundational dimensional data models, enterprise metrics, and curated data products on a modern lakehouse. Partner with business domains to translate processes into scalable, high-quality datasets, optimize analytical workloads, and operate distributed processing systems to support analytics, BI, and AI.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

As a Data Engineer, you will design and implement data solutions, automate processes, and support data analytics for the Department of Defense.

Design, develop, and maintain batch and streaming data pipelines and ETL for large unstructured datasets. Collaborate with developers, data scientists, and analysts to build data management systems, containerize and deploy services (Docker/Kubernetes), and implement CI/CD, DevOps, and monitoring on Linux/AWS infrastructure for intelligence community missions.

Design, build, and optimize enterprise-scale data models, pipelines, and cloud-native architectures to support analytics, AI/ML, and reporting. Lead data governance, metadata management, lineage, access controls, and data quality. Partner with stakeholders to translate business needs into scalable, secure, and high-performance data infrastructure, and evaluate emerging technologies to evolve the data platform.

Design, build, test, and operationalize data pipelines and solutions on cloud and Databricks. Ensure data quality, governance, security, and support incident resolution. Collaborate cross-functionally to deliver analytics, ML/AI enablement, and educate users on data products.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Design, build, and maintain scalable data solutions for enterprise counterparty credit risk. Lead and deliver moderately complex technical initiatives, collaborate with risk and business stakeholders, develop code/tests/documentation, support reporting and dashboards, and mentor junior staff.

Lead design, development, and support of scalable data solutions for counterparty credit risk. Build and maintain data pipelines, reports, and dashboards; collaborate with business stakeholders; resolve technical challenges; lead projects and mentor junior staff; ensure compliance with risk and regulatory requirements.

Lead and mentor a data engineering team to design, build, test, and support cloud-based, real-time data platforms and distributed microservices using modern big data, streaming, and data warehousing technologies to deliver scalable customer and enterprise experiences.

The Data Engineer will design and maintain data pipelines on Azure, manage ClickHouse for analytics, and ensure healthcare compliance while supporting ML integration.

Lead the design and migration of a data platform architecture. Manage data pipelines, mentor teams, and ensure platform reliability.

As an FP&A Data Engineer, you will manage data pipelines, develop financial models, and automate reporting to assist management and teams in making data-driven decisions.

Design, build, and maintain scalable relational databases, data warehouses, and integration workflows. Optimize SQL performance, ensure data integrity/security, support reporting/analytics, and collaborate with business stakeholders for timely, compliant reporting and continuity efforts.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Hands-on technical lead for global Azure-based data analytics environments. Own escalation support for complex incidents, performance/scalability troubleshooting on large datasets and pipelines, guide support analysts, and partner with engineering to drive durable fixes across Databricks, SQL Server/DWH, and Azure data services.

Design, develop, and monitor large-scale data pipelines and cloud-native data applications; collaborate with consulting teams and clients; apply best practices (TDD, CI/CD); build and operationalize ML-ready data platforms; produce documentation and runbooks; mentor junior engineers and contribute to proposals and analytics assets.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

The role involves building data pipelines, processing structured and unstructured data, and collaborating with a multidisciplinary team on technology solutions for clients in various sectors.