Top Senior Data Engineer Jobs

Leads the design, implementation, and support of a near-real-time transactional cache platform. Builds CDC pipelines from UniVerse to PostgreSQL, transforms data into optimized MongoDB models, and supports API-serving workloads on GCP. Owns performance, reliability, observability, incident response, monitoring, and operational support. Provides technical leadership, establishes engineering standards, drives modernization, collaborates with application teams, and mentors engineers across data platform initiatives.

Designs, develops, and supports scalable ETL pipelines and large-scale data platforms using Python, PySpark, SQL Server, SSIS, and Hadoop technologies. Responsibilities include database performance tuning, data modeling, production troubleshooting, batch scheduling, data-quality controls, CI/CD, secure development, and collaboration across technical teams. The role also involves mentoring developers and evaluating AI-assisted development tools.

Leads the design, development, optimization, and security of very large SQL Server databases and data warehouses. Oversees ETL and Microsoft BI pipeline development, data modeling, performance tuning, code reviews, and access management. Collaborates with DBAs, mentors developers, applies AI-assisted optimization, assesses risks, and drives scalable, reliable data products.

Leads development and maintenance of telematics data systems supporting predictive and condition-based maintenance. Builds ETL pipelines, databases, integrations, semantic models, and Power BI reports; analyzes asset health data to identify failure patterns and improve reliability. Maintains data quality and workflow performance, develops predictive maintenance thresholds, performs root cause analysis, and collaborates with engineering, maintenance, reliability, and IT teams to optimize asset performance.

Designs, develops, tests, and documents scalable data architectures, applications, ETL pipelines, and data warehouses. Improves data delivery infrastructure, automates processes, troubleshoots data issues, and ensures data reliability, quality, and validation. Collaborates with developers, architects, analysts, data scientists, and business teams to deliver effective data solutions. The role also requires database performance tuning, maintenance, monitoring, and occasional work outside normal hours.

Build and maintain data lakes, data warehouses, and modern data pipelines for a multi-tenant SaaS platform. Design end-to-end data processes, write pipeline and infrastructure code, conduct design reviews, optimize performance, monitor systems, support data governance, and troubleshoot scalability and security issues. Collaborate with technical and business stakeholders, mentor junior engineers, evaluate technologies, document platform architecture, automate testing, and participate in on-call rotation.

Design and build a company-wide data platform supporting ingestion, CDC, replication, transformation, governance, semantic discovery, analytics, and self-service dbt workflows. Develop scalable architectures across distributed SQL databases and analytical platforms, establish data quality and observability standards, implement access controls and metadata foundations, and provide technical leadership across engineering teams. The role also shapes source-of-truth architecture and enables data for AI and agentic applications.

Develop and monitor scalable analytics frameworks for Pega Customer Decision Hub using Databricks, Python, PySpark, and SQL. Build reusable notebooks, pipelines, queries, and monitoring solutions for customer interaction analytics, model performance, attribution, propensity analysis, and engagement effectiveness. Standardize analytical processes, support back-testing and KPI development, and enable self-service analytics across marketing and enterprise teams.

Designs, builds, and operates large-scale Hadoop data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark-based processing solutions; managing streaming workflows; working with relational and NoSQL databases; orchestrating jobs; troubleshooting distributed systems; and supporting reliable, scalable production data platforms. Preferred experience includes cloud Hadoop environments, lakehouse formats, governance tools, Kubernetes, CI/CD, and infrastructure as code.

Build and own trusted data products, dimensional models, semantic layers, and dbt transformations that support analytics, experimentation, machine learning, reporting, and decision-making. Partner with cross-functional teams to translate business needs into scalable datasets, improve data quality and governance, establish engineering standards, and drive self-service analytics. Mentor engineers and analysts while leveraging AI tools to improve development, documentation, and quality.

Design, build, and operate WHOOP’s data platform infrastructure, including lakehouse, warehouse, and streaming systems. Improve performance, reliability, cost efficiency, observability, and developer experience across AWS-based data systems. Develop internal tools, APIs, automation, CI/CD, and infrastructure-as-code practices. Partner with data, ML, analytics, and software engineering teams to support scalable workflows, governance, and production excellence while using AI-assisted development tools effectively.

Own the architecture, roadmap, and operation of Clarium’s AWS-based data platform. Build reliable batch and near-real-time pipelines, model healthcare supply chain data, optimize Postgres and Snowflake performance, and establish standards for testing, deployment, quality, and governance. Partner cross-functionally to create durable data models, lead multi-quarter initiatives, manage sensitive data controls, and mentor engineers.

Leads the architecture, development, operation, validation, and optimization of high-volume ETL and data migration pipelines. Transforms structured and unstructured data from legacy systems into cloud platforms, troubleshoots failures, performs data quality validation, conducts root cause analysis, and documents migration processes. Collaborates with cloud, DevSecOps, database, QA, and product teams while reporting pipeline performance, migration status, and data quality metrics.

Designs, builds, and optimizes scalable data pipelines, warehouses, lakes, and platforms integrating complex network operations data. Uses Kafka, Elastic Stack, AWS, Kubernetes, and automation to support ingestion, transformation, analytics, dashboards, security, and compliance. Responsibilities include ETL/ELT, data validation, CI/CD, performance tuning, troubleshooting, documentation, and collaboration with analysts and visualization engineers. The role requires an active TS/SCI clearance, Security+ certification, and extensive data engineering experience in DoD or similarly secure environments.

Designs, builds, and optimizes scalable data pipelines, warehouses, lakes, and ingestion platforms using Kafka, Elastic Stack, AWS, and Kubernetes. Integrates disparate network operations data while ensuring quality, security, and DoD compliance. Automates ETL/ELT, validation, monitoring, CI/CD, and deployment processes, supports dashboards and analytics, troubleshoots platform performance, and documents architecture and operational procedures.

Designs, builds, and maintains scalable data pipelines, warehouses, lakes, and integrations using Kafka, Elastic Stack, AWS, and Kubernetes. Optimizes ingestion, querying, indexing, storage, and platform performance while supporting visualization, CI/CD, automation, security, and DoD/DISA compliance. Collaborates across engineering, analytics, software, and network operations teams, documents architectures and procedures, and resolves data-platform issues in classified enterprise environments.

Builds and maintains scalable data pipelines, warehouses, and lakes for analytics and reporting. Integrates network operations data using Kafka, Elastic Stack, AWS, and OpenShift/Kubernetes; develops ETL/ELT automation, dashboards, CI/CD processes, and data validation workflows. Optimizes ingestion, queries, indexing, storage, and cluster performance while ensuring security and DoD/DISA compliance. Troubleshoots platform issues, collaborates with analysts and visualization engineers, and documents architectures and operational procedures.

Leads and mentors the Data Engineering team while remaining hands-on in designing, building, and maintaining scalable data pipelines, warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical delivery; and resolves complex issues. Partners with Finance and business stakeholders to translate data needs into practical solutions, sets technical direction, evaluates technologies, and communicates strategy, risks, and recommendations to leadership.

Develops and maintains data architectures, databases, data warehouses, ETL pipelines, migrations, stored procedures, replication, and data governance processes. Collaborates with analytics teams and business stakeholders to define requirements and enable business intelligence, reporting, and predictive modeling. Maintains data infrastructure across Azure and on-premises environments while improving performance, reliability, quality, and CI/CD practices.

Designs dimensional models, analytical datasets, ETL/ELT workflows, data warehouse and lake integrations, and governed Power BI semantic models. Ensures data quality, lineage, security, observability, and performance through testing, documentation, CI/CD, monitoring, and optimization. Partners with stakeholders to define requirements, KPIs, and acceptance tests while enabling self-service analytics and data-informed decision-making.

Senior Data Engineer responsible for designing scalable data pipelines and ELT/ETL workflows using PySpark, Snowflake, AWS, and SQL. The role supports insurance analytics, cloud migration and modernization, data warehouse modeling, data quality validation, performance optimization, and technical standards. The engineer will troubleshoot complex data systems, develop transformations and stored procedures, and document pipeline processes.

Leads and delivers complex data engineering projects, designing scalable data infrastructure, pipelines, warehouses, and data quality solutions. Recommends tools and architectures, optimizes data movement and performance, collaborates across technical teams, mentors engineers, and translates technical concepts for clients and nontechnical stakeholders. The role may also involve real-time processing, data modeling, transformation, security, and MLOps.

Design, develop, test, and maintain ETL solutions and data warehouse structures using SSIS and SQL Server. Build and support SSAS models, optimize ETL performance, ensure data quality, partner with stakeholders, and produce technical documentation to support BI and reporting initiatives.

Designs, builds, and maintains scalable ETL, batch, and streaming data pipelines using Airflow, Kafka, Python, AWS S3, Trino, OpenSearch, and related technologies. Develops data APIs, optimizes storage and retrieval, supports data governance, and integrates classified data securely. Collaborates with software developers, analysts, and product teams to deliver reliable data solutions for production applications. This hybrid Fort Bragg role requires active TS/SCI clearance, CI Poly eligibility, and U.S. citizenship.

Design, develop, and optimize enterprise-scale data platforms, pipelines, data lakes, warehouses, and analytical datasets on Azure and Snowflake. Build ETL/ELT processes using ADF, Databricks, PySpark, SQL, Python, and Snowflake. Manage data models, governance, security, quality, CI/CD, cloud migrations, and performance optimization. Collaborate with stakeholders, troubleshoot production issues, document solutions, and support Agile delivery.