Top Senior Data Engineer Jobs

Design, build, operate, and maintain production ETL/ELT pipelines, integrations, and curated datasets across Salesforce, Deltek, Autodesk, Azure, and Microsoft Fabric. Implement data models, orchestration, API-first services, monitoring, data quality, governance, and support AI/ML and BI initiatives while partnering with stakeholders to ensure secure, reliable data delivery.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

Lead hands-on data engineering and architecture for a classified on-prem Analytics Core: build ETL/ELT pipelines, clean and model 30 years of multi-modal and geospatial data, integrate ESRI spatial data, establish AI/ML feature standards, enforce DoD-aligned governance, and mentor transitioning staff to operationalize the solution.

Design, build, operationalize, and maintain enterprise-scale data applications and scalable ETL/ELT pipelines. Develop and troubleshoot Palantir Foundry transformations, ontology objects, and logs. Collaborate with cross-functional teams to convert structured and unstructured data for analytics, monitoring, and platform operations.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Design, build, and maintain enterprise data warehouse and medallion-layer dbt Core models. Integrate MDM workflows, develop scalable pipelines using Microsoft Fabric/Azure Data Factory, optimize Snowflake performance, ensure data quality, support CI/CD and monitoring, troubleshoot production issues, and collaborate with cross-functional stakeholders to deliver analytics-ready datasets.

Design, build, and operate scalable data pipelines and integrations for RCTs and voter datasets; extend foundational data, optimize orchestration for cost/reliability, load-test for election traffic, and build AI tooling for QC and evaluation. Lead cross-team projects, ensure high quality through testing and code review, document systems for non-engineers, and rotate through on-call and rapid-response during high-stakes moments.

Design, build, and maintain scalable data pipelines and BigQuery-based data lake solutions on GCP. Optimize pipelines for performance and cost, enforce data quality, develop schemas and models, automate with Python and Terraform, integrate CI/CD, monitor and troubleshoot infrastructure, and collaborate with analysts and data scientists to deliver analytics-ready data. Document processes and apply healthcare PHI governance and security best practices.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Build and scale robust production data pipelines and automate complex game-related datasets, ensure high data quality, design schemas and data models, collaborate with logging infrastructure and analytics stakeholders to enable analysis across Netflix's games library.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Design, build, and maintain scalable end-to-end data ingestion and curation pipelines on GCP. Ensure data quality, governance, security, and cost optimization. Use Python, SQL, dbt/Dataform, Astronomer, and Terraform to automate workflows and infrastructure. Collaborate with architects and stakeholders, document processes, and translate business requirements into production-ready data assets for analytics and AI consumption.

Lead data engineer responsible for designing and building scalable databases, ETL pipelines, and data messaging platforms. Develop and optimize SQL/stored procedures, implement Spark/Snowflake/Databricks solutions, automate processes with Python and shell scripts, and collaborate with global business stakeholders using Agile practices to deliver accurate, accessible data for operational stores, archives, and analytics.

The Senior Research Engineer will curate large datasets, automate data pipelines, optimize queries, and support data-driven research impacting business outcomes.

The Senior Lead Data Engineer designs and develops technical solutions, leads a team of developers, and utilizes big data and cloud technologies to deliver robust solutions for Capital One.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

Lead a team of engineers to design and implement data solutions using cloud technologies, optimizing data pipelines and collaborating with Agile teams to deliver impactful data-driven products.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data publication and transformation solutions. Collaborate across Agile teams to build distributed, real-time data pipelines and data warehousing (Redshift/Snowflake), perform unit tests and code reviews, mentor engineers, and adopt emerging technologies to deliver scalable data platforms.

Design, build, and optimize HANA-native data models (calculation views, CDS, AMDPs, SQLScript); tune HANA performance; implement security; manage replication and tiering; support HANA applications consumed by Fiori/BW/BTP; lead capacity planning, documentation, audits, and mentor junior developers.

Build and operate large-scale data pipelines (batch and streaming) for multi-terabyte audio/video datasets. Deploy and scale containerized workloads on Kubernetes/AWS, maintain Spark/Ray jobs, run workflow orchestration (Airflow), tune enterprise databases, and collaborate with ML teams on training and MLOps workflows.

Lead data engineering efforts to design, build, deploy, and maintain scalable data platforms and pipelines. Guide cross-functional teams, support ETL/ELT workflows, parse structured and unstructured data, develop scripts to convert data, and operate/monitor enterprise-scale data applications for analytics and reporting.

Design, build, and operate scalable finance data pipelines and analytics-ready datasets. Support ELT/ETL, data quality, governance, semantic models, Power BI reporting, forecasting, ARR analytics, and AI-enabled analytics. Partner with finance stakeholders to translate requirements and maintain production-grade, monitored data solutions.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.