Top Senior Data Engineer Jobs

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 and maintains scalable cloud data pipelines, ELT/ETL workflows, data models, ingestion frameworks, and automation systems supporting cloud cost visibility and optimization. Uses Python, SQL, dbt, Airflow, AWS services, and Snowflake. Optimizes performance, reliability, storage, and compute costs while ensuring data quality, validation, auditability, and production troubleshooting. Partners with Engineering and Finance stakeholders on cost-aware decision-making and usage-based insights.

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 and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process telemetry and usage data. Optimize performance, reliability, and cost while ensuring data quality, validation, and auditability. Partner with Engineering and Finance stakeholders on cost-aware decision-making and usage insights.

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.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process telemetry and usage data. Optimize performance, reliability, and cost; ensure data quality, validation, and auditability; and partner with Engineering and Finance stakeholders on usage-based insights and cost-management initiatives.

Develop and operationalize data applications using Palantir Foundry for national security clients. Translate stakeholder needs into technical solutions, ingest and transform data, build workflows and applications, support scalable data platforms, and lead analytics projects. The role also involves advising clients on data-driven mission impacts, collaborating with Agile teams, and applying DevSecOps, AI/ML integration, security, and compliance practices.

Build and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include designing data solutions, analyzing and cleansing data, implementing governance and security practices, testing transformations, resolving defects, and collaborating across teams. The role requires Python, SQL, AWS, Databricks, Terraform, API design, and AI platform experience.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and maintain scalable data platforms, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop scripts and programs to convert data into usable formats, support platform operations, and collaborate with analysts, engineers, developers, and data consumers in agile environments. Projects support missions including fraud detection, cancer research, and national intelligence.

Builds and maintains reliable, scalable, secure data platforms and cloud infrastructure. Responsibilities include troubleshooting Tier 3 production issues, designing data ingestion and transformation workflows, implementing CI/CD, monitoring systems, and supporting enterprise data operations. The role may involve Kubernetes, Kafka, NiFi customization with Java, AWS services, and classified Intelligence Community environments.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.

Develop and maintain ETL pipelines integrating systems such as Canvas and Qualtrics; automate data-quality and analytics workflows using Python, SQL, and cloud-based tools. Diagnose data issues, create business intelligence solutions, translate stakeholder needs into analytical requirements, and document and train users on implemented systems. The role also supervises student employees and supports reporting, surveys, accreditation, and process improvement.

Designs, builds, and manages a Snowflake-based business intelligence platform using dbt, SQL, data modeling, pipelines, and transformation tools. Performs business and systems analysis, fit-gap analysis, security and permissions testing, technical specifications, solution development, system and user acceptance testing, troubleshooting, documentation, training, and risk management. Collaborates with reporting, functional, PeopleSoft, database, security, and other project teams to modernize citywide analytics and data sharing.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Lead the design, development, deployment, and operation of scalable data pipelines, APIs, services, and healthcare analytics applications. Translate business and analytical requirements into reliable production solutions, establish technical standards and architecture, troubleshoot issues, improve performance, document designs, coordinate platform changes, and provide technical leadership and mentorship to data engineers.

Design, integrate, and implement on‑prem and cloud data warehousing solutions; process payer files into the data warehouse; develop pipelines using SQL, Python, and PySpark; troubleshoot and optimize data performance; collaborate with engineers, analysts, data scientists, and product teams to deliver high‑quality modern data solutions.

Build and operate a scalable data platform supporting pipelines, reporting, and analytics across business teams. Responsibilities include defining canonical data models, implementing workflow orchestration with tools such as Airflow, improving platform infrastructure, troubleshooting complex data and system issues, enforcing data security controls, and mentoring junior engineers. The role requires strong software engineering, cloud, infrastructure, communication, and data pipeline expertise.