Top Senior Data Engineer Jobs

As a Senior Lead Data Engineer at Capital One, you'll lead a development team, utilizing various big data and cloud technologies to deliver impactful solutions. Responsibilities include collaborating with Agile teams, mentoring, and ensuring optimal code performance.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

Design, build, and optimize Databricks-based data pipelines and Delta Sharing solutions on Azure. Perform data profiling, transformation, QA, and prepare data for analytics and MLOps. Collaborate with stakeholders, prototype data products, support data lakes/warehouses and BI dashboards, mentor junior staff, and support federal emergency management analytics modernization.

Design, implement, and maintain scalable data pipelines and platforms for structured and unstructured data to support AI/ML workloads. Ensure data governance, security, monitoring, and CI/CD for ETL workflows across on‑prem, cloud, or hybrid environments while collaborating with cross‑functional technical teams.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

The Data Engineer IV is responsible for designing and building data pipelines, implementing transformation logic, ensuring data quality, optimizing data infrastructure, and collaborating with stakeholders. This role requires advanced skills in data engineering, management of projects, and mentoring of junior developers.

Design, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

Design, build, deploy, and maintain scalable data pipelines and platforms. Integrate and analyze structured and unstructured data from APIs and databases, guide cross-functional teams, and apply data mining, statistical analysis, and AI/ML to support mission-driven projects.

Technical owner of the Microsoft Fabric and OneLake data platform: design medallion architecture, manage ingestion and transformations, enforce CI/CD and engineering standards, ensure monitoring/performance/cost optimization, implement data quality/lineage/security, enable GenAI via Azure AI and Foundry (RAG), and lead/vendor/manage engineering hires and outsourced partners.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Design and implement scalable data ingestion, ETL/ELT pipelines and transformations into Microsoft Fabric using ADF and notebooks (Python/Pandas/Spark). Build and maintain dimensional data warehouse models, ensure data quality, performance tuning, and prepare data for semantic models. Collaborate with stakeholders, participate in code reviews, documentation, and operational handoffs while establishing standards for deployment and governance.

The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

The Lead Data Engineer will develop and optimize data pipelines, mentor the engineering team, and ensure data integrity and compliance while collaborating on business insights.

Designs, builds, and operationalizes complex Databricks/AWS data solutions and pipelines. Performs data analysis, cleansing, testing, and incident resolution while incorporating governance, security, and quality. Collaborates across teams, supports end users, and provides technical leadership to deliver production-ready data products for analytics, ML, and AI use cases.

Lead design and execution of advanced analytical projects, architect scalable data pipelines and analytic solutions, develop and validate mathematical algorithms and models, create dashboards and visualizations, integrate analytics into operations, mentor junior staff, and direct technical teams to deliver high-impact customer solutions.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Freelance Senior Data Engineer to implement and support Adobe Experience Cloud solutions (CJA, Launch, Journey Optimizer, Adobe Analytics). Design and maintain tracking, data pipelines, and data orchestration; work with CDPs for ingestion and activation; validate data quality; troubleshoot implementations; document data flows; collaborate with engineering, analytics, and business teams. Short-term remote contract (US only, excluding NM and HI).

Design, build, and operationalize complex data pipelines and products to support analytics and AI/ML. Analyze and transform source data, enforce data governance/security/quality, resolve high-complexity incidents, mentor team members, and partner with business stakeholders to deliver reusable, scalable data platform capabilities.

Design, build, and support scalable data platforms and pipelines (ETL, warehousing, APIs) using DBT, Snowflake, and SQL. Collaborate with architecture, security, and application teams, build CI/CD, provide escalated on-call support, mentor staff, and deliver data solutions for the bank.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Design and maintain data models, build and optimize data pipelines and database infrastructure, integrate data with BI tools, ensure data quality and governance, troubleshoot performance and downtime, and collaborate with stakeholders to deliver reliable analytics and reporting.

Design, build, and optimize large-scale Databricks/Spark/Delta Lake data pipelines and Redshift data platforms. Implement ETL/ELT patterns, ingest telemetry and behavioral data (Matomo, FullStory, Pendo), improve performance and observability, provide L2/L3 production support and RCA, and mentor engineers while shaping data architecture and best practices.

Design, build, and maintain enterprise-scale data platforms and scalable ETL/ELT pipelines. Develop and operationalize data transformations in Palantir Foundry, debug logs, troubleshoot issues, and convert varied data into usable formats. Guide cross-functional teams, support platform scaling, monitoring, and operations, and apply PySpark/Spark SQL for analytics and reporting to support mission-driven use cases.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze massive structured and unstructured data to support enterprise analytics, ML, and reporting. Ensure reliable, performant production data platforms with troubleshooting, documentation, and orchestration.