Top Senior Data Engineer Jobs

Design and build a single data platform and pipelines, ensure infrastructure and standards, provide governance and consultancy, and work with stakeholders to expand data capabilities across the insurance business.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, build and modernize ELT pipelines (including SAP integration), create analytics-ready data models, ensure HA/DR and recovery objectives, and operationalize data governance and privacy (GDPR/CCPA). Serve BI, reporting, and AI consumers while partnering with architecture, DBA, and GRC teams.

Build and maintain enterprise data platform and ETL/ELT pipelines, design and deploy data models, ensure data quality and governance, integrate third-party sources, and support analytics and AI initiatives across the organization.

Design, develop, and maintain enterprise ETL pipelines and scalable data processing using ADF, Informatica, Python, Spark/Databricks, and relational systems. Support Medicaid analytics, data validation, cloud migrations, production operations, and CI/CD practices.

Design, build, and maintain a petabyte-scale open-source data lakehouse and end-to-end data pipelines (ingest → transform → consume) ensuring performance, reliability, and data quality for analytics workloads.

Lead architecture and implementation of cloud-based data ingestion and ETL solutions. Build and maintain ETL pipelines, collaborate with stakeholders, create design documentation and tests, ensure governance and pipeline reliability, and coordinate with onshore/offshore engineering teams.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Lead development and operations of geospatial data pipelines and lakehouse systems. Migrate orchestration to Prefect, extend sources and transforms, optimize Postgres and Iceberg stores, build observability and AI-agent readiness, and integrate cross-team tooling with product and ML teams.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, design HA/DR and recovery plans, build resilient ELT pipelines from sources including SAP, create analytics-ready data models for BI/AI, operationalize data governance and privacy (GDPR/CCPA), and mentor DBA succession while delivering documentation and runbooks.

Build and operate production data infrastructure: ingest, transform, and route large-scale streaming and batch data from heterogeneous enterprise sources; design ontology/semantic models and tuple/graph representations; ensure isolation, data quality, observability, and low-latency delivery for ML and product teams.

Design, build, and maintain data architectures, ETL pipelines, and data models to enable analytics across Marketing, Finance, Sales, and Logistics. Implement integrations from cloud sources, secure API connections, perform large-scale data mining and entity resolution, translate algorithms into code, and use SSIS and BI tools to deliver reliable reporting and dashboards.

Design, build and scale a 500+ TB Audience Management Big Data platform. Own data ingestion, validation, transformation and storage pipelines. Write server-side Spark/Scala code, perform R&D on new technologies, mentor engineers, and partner with product managers to meet roadmap and business needs.

Lead design and implement scalable data infrastructure and transformations. Build data repositories, convert data science algorithms into robust pipelines, mentor a small data engineering team, ensure delivery quality, and collaborate with Product, Data Analysts, and Data Scientists.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.

Design, build, and maintain scalable, fault-tolerant identity and device data pipelines and backend systems. Modernize data models, improve code quality and test coverage, support production operations and on-call, collaborate with product and cross-functional teams, and participate in code reviews and agile ceremonies.

The Sr. Data Engineer will develop data pipelines, automate data processing, collaborate in Agile teams, and ensure data extraction reliability while adhering to engineering principles.

The Sr. Data Engineer will manage data pipelines, develop ETL processes, support continuous automation, ensure data reliability, and work collaboratively in Agile teams to meet client needs.

The Senior Data Engineer will oversee data operations, design scalable data pipelines, ensure data quality, and support cross-functional data initiatives.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

Design, build, and operate a privacy-safe, cloud-based data platform supporting batch and real-time pipelines, analytics, ML/AI, governance, observability, and high pipeline reliability. Partner with stakeholders, set data engineering standards, troubleshoot production incidents, and mentor engineers.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

The Senior Data Engineer will develop and optimize software applications and data platforms for NAVOCEANO, supporting mission-critical solutions, data pipelines, and analytics capabilities.

Design, build, and maintain ETL/ELT data pipelines and domain-oriented data products to support a Digital Thread. Ensure data quality, governance, mapping, and discoverability. Apply software-engineering practices (version control, testing, CI/CD, IaC) for production deployments. Integrate manufacturing and operational systems and prepare AI/ML-ready datasets for analytics and predictive use cases.

The Senior Data Engineer will develop data pipelines, analyze geospatial data, implement quality assurance processes, and support database synchronization with APIs, contributing to climate risk financial modeling.