Top Senior Data Engineer Jobs

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.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Build and scale core data infrastructure and foundational datasets. Design, develop, and maintain scalable data pipelines for analytics, ML, experimentation, and product use cases. Implement data quality, audits, alerting, and anomaly detection. Partner with product, engineering, and business stakeholders and help establish long-term, scalable data architecture and tooling.

Build production-ready analytics and ETL solutions to support Internal Audit. Perform data modeling, engineering, governance, entitlement management, and write analysis code to improve audit sampling, detect control gaps, and automate audit processes.

Design, build, and operate enterprise-scale data ingestion and transformation pipelines in Microsoft Fabric/Azure. Lead legacy migrations, implement data quality, monitoring, and governance, optimize pipelines for performance and cost, onboard data domains, and prepare high-quality data assets for Power BI and downstream analytics while documenting standards and collaborating with cross-functional teams.

Lead design, development, and optimization of Snowflake-based data platforms and scalable ELT/ETL pipelines. Build dimensional models, data marts, and curated analytical datasets; implement Snowpark, stored procedures, streams, tasks; enforce data quality, lineage, governance, and security; automate orchestration with Airflow/Azure Data Factory/AWS Glue; support finance, risk, compliance, and regulatory reporting; mentor junior engineers.

Design, build, and maintain scalable data pipelines and data warehouse solutions on Azure. Perform source data analysis, profiling, ETL development with PySpark, support migrations from on-prem to cloud, troubleshoot production issues, collaborate in Agile teams, and contribute to ML/Generative AI initiatives and Power Apps development.

Lead design, implementation, automation, and maintenance of large-scale ETL pipelines and scalable data solutions. Optimize SQL and database performance, collaborate with analytics and business stakeholders to translate needs into data requirements, apply best practices (version control, code reviews, data validation), mentor junior engineers, and ensure delivery of accurate, robust data products while complying with internal policies and regulations.

Design, build, and maintain data pipelines and Azure data warehouse infrastructure. Ensure data quality, governance, performance tuning, automation, and provide BI insights using Power BI or QlikSense.

Lead and mentor a data engineering team to design, build, test, and operate cloud-based, distributed data platforms and pipelines using big-data, streaming, and data‑warehousing technologies to deliver production solutions.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.

Build and implement a transactional data system: design and develop SSIS ETL packages, consume web services in ETL, create SSRS reports for ETL status, and implement T-SQL/SQL Server solutions per functional requirements and system design.

Design, build and maintain a data transactional system performing ETL and web service calls to synchronize state between systems. Develop SSIS packages, data models, dictionaries, test plans, and assist architects on database strategy.

Implement OLTP and OLAP systems to persist court information from functional requirements and system design. Perform ETL, export data to external agencies, use managed service calls, design and model database structures, design/code/deploy SSIS packages, document data dictionaries and package definitions, develop SSIS test plans, and support architects.

Design, build, and maintain enterprise-scale data platforms and ETL/ELT pipelines. Develop software and scripts to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to scale, monitor, and operate data solutions using Databricks, Spark, and CI/CD practices to support mission-driven analytics.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion (bronze/silver/gold) lakehouse architectures. Ensure data quality, lineage, governance, and performance for large federal health datasets. Collaborate with data scientists, analysts, and CMS stakeholders and support Agile development and CI/CD workflows.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

As a Lead Data Engineer, you will design and develop solutions, collaborate with teams on data technologies, and support cloud-based data warehousing and big data applications.