Top Data Engineer Jobs

Build and maintain scalable data infrastructure and ETL/ELT pipelines using Python, SQL, NiFi, Linux, and database technologies. Integrate structured and unstructured data, ensure data quality and security, optimize systems, and document reproducible solutions. Collaborate with stakeholders, deliver technical briefings, use machine learning and NLP techniques, and explain complex concepts to junior data scientists. The role is full-time, onsite in McLean, Virginia, and requires an active TS/SCI clearance with CI polygraph.

Own and evolve the club’s cloud-based data platform, building scalable pipelines for performance analytics, reporting, recruitment, opposition analysis, and sports science. Perform database administration, including tuning, indexing, backups, and access management. Develop AI agents for pipeline monitoring, maintain code quality through Azure DevOps, and ensure data governance, privacy, quality, documentation, and platform support while collaborating with data scientists, coaches, and sports science teams.

Develop and maintain scalable data stores, ETL/ELT workflows, data pipelines, and platforms for structured and unstructured data. Build software to retrieve, parse, process, and convert data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile environments while supporting platform scaling, monitoring, and operations. The role requires a Secret clearance and may involve cloud, distributed computing, streaming, NoSQL, and data warehousing technologies.

Build and maintain cloud-based data pipelines, data models, lakehouse layers, and analytics-ready datasets. Collaborate with stakeholders to translate requirements into technical solutions, ensure data quality and lineage, monitor pipeline performance, investigate failures, and document remediation. Support Microsoft Fabric and Azure data platforms while meeting security, governance, audit, and government ICT requirements. Contribute to reusable enterprise data assets for reporting, analytics, data sharing, and future AI-enabled use cases.

Senior data engineer responsible for building Azure and Microsoft Fabric ETL/ELT pipelines, Lakehouse and Warehouse architectures, dimensional models, and ML data pipelines. Develops PySpark, Python, and SQL transformations; manages Azure DevOps CI/CD and Bicep infrastructure; and implements security, governance, data quality, lineage, monitoring, and performance optimization. Collaborates with data science, architecture, DevOps, security, QA, and business teams while modernizing legacy SAS and file-based processes.

Develop, test, evaluate, maintain, and integrate software and computer hardware systems for the FAA WAAS program. The role requires applying Python, MATLAB, Tableau, and C; debugging complex systems; conducting testing, reviews, and audits; supporting configuration management; and collaborating independently and within an agile team. This is an on-site Oklahoma City position with less than 10% travel and requires eligibility for an FAA Public Trust clearance.

Develop and optimize SQL Server and Snowflake data solutions, migrate workloads to the cloud, and build ETL/ELT pipelines using SQL, Python, Airflow, and AWS. Responsibilities include performance tuning, data validation, reconciliation, monitoring, production troubleshooting, and support for transaction cost analysis products using large-scale financial, trade, and market data.

Build and operate the Global Card team’s data foundation, including pipelines, schemas, data marts, feature pipelines, quality monitoring, and data contracts. Lead architecture and technical direction, partner with product, analytics, and data science teams, contribute to backend services, establish engineering standards, mentor colleagues, and ensure reliable, secure, scalable data systems.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Designs and supports modern data platforms using Microsoft Fabric and Databricks. Builds scalable ingestion, transformation, orchestration, lakehouse, semantic modeling, and analytics solutions. Translates client business requirements into technical designs, optimizes SQL and Spark workloads, ensures data quality and performance, and enables Power BI reporting. The role is client-facing, collaborative, and consulting-oriented, requiring stakeholder communication, documentation, and adherence to engineering and delivery best practices.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Design, build, and optimize scalable batch and streaming data pipelines using Fivetran, Databricks, Spark, Azure, Kafka, and Kubernetes. Develop source integrations, improve data delivery and infrastructure scalability, monitor and troubleshoot production workloads, and ensure SLA compliance. Contribute to architecture and technology decisions, automate deployments and monitoring, collaborate across teams, and mentor junior engineers supporting intelligent manufacturing analytics.

Develops and maintains Python data pipelines and business data objects. Designs software solutions, implements source-to-target mappings, builds CI/CD pipelines, and performs unit and integration testing. Resolves complex technical problems, interprets design requirements, and promotes software quality and team improvement. The role requires onsite work five days per week in Chicago, Peoria, or Dallas.

Lead the rebuild and modernization of the company’s data platform by designing and implementing an Azure and Databricks lakehouse. Responsibilities include managing Azure Data Factory pipelines, developing Delta Lake architectures, creating data models and transformations with SQL and Python, establishing development standards, monitoring data quality, troubleshooting performance issues, leading technical projects, and mentoring or hiring data engineering staff.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Designs, builds, and maintains production-grade data pipelines, ETL/ELT workflows, data models, and enterprise data platforms. Integrates databases, APIs, files, and third-party systems using Microsoft Fabric and related technologies. Ensures data quality, security, governance, monitoring, performance, and operational reliability. Collaborates with stakeholders and technical teams to deliver sustainable data solutions, document architectures, troubleshoot issues, and support continuous improvement.

Own and evolve the UFL’s data platform, including Fivetran, dbt, BigQuery, Cloud Run, ETL/ELT, reverse-ETL pipelines, and data models. Maintain reliable data flows, lead migration data mapping and validation, improve architecture and data quality, and establish engineering standards. Partner with Sales, Marketing, Finance, Sponsorships, and Football Operations to deliver trusted datasets and scalable analytics foundations.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build and maintain scalable data pipelines, transformations, semantic models, telemetry datasets, dashboards, and operational reporting. Partner with engineering, product, support, and leadership teams to define metrics and data quality standards. Develop validation and reliability checks, integrate diverse data sources, automate reporting workflows, improve data governance and documentation, and investigate reporting anomalies. Support release readiness, customer health, feature adoption, supportability, and escalation analytics.

The Senior Data Engineer designs, develops, and maintains scalable data pipelines and infrastructure using AWS Glue, Kafka, and Step Functions. Responsibilities include ETL development, data modeling, data quality, troubleshooting, compliance, and collaboration with cross-functional teams. The role also mentors Data Engineers and supports analytics and business intelligence initiatives. Required expertise includes SQL, Python, AWS services, data warehousing, and ETL tools such as Informatica, Talend, or SSIS.

Build, deploy, and operate production Spark pipelines on Databricks powering analytics products. Partner with product teams on pipeline requirements, data quality, freshness, monitoring, and observability. Diagnose performance and cost issues, establish scalable pipeline standards, and support streaming, logical replication, RAG pipelines, AWS infrastructure, and platform administration as needed.

Designs and maintains Microsoft Fabric data pipelines, medallion Lakehouse architecture, data models, governance, orchestration, and quality processes. Integrates ERP, HRIS, MRP, finance, project, employee, service, and manufacturing data into reliable data products. Supports reporting, business intelligence, AI and machine learning use cases, including RAG and vector embeddings. Troubleshoots pipeline issues, manages Fabric capacity and documentation, and collaborates with analysts, data scientists, developers, DevOps teams, and business system owners.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include data transformation, schema and business-rule enhancements, load monitoring, troubleshooting data availability, evaluating emerging technologies, and collaborating with business, engineering, and privacy teams. The role also supports data governance, secure data handling, cloud technologies, and adoption of AI-enabled tools.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Builds ETL processes, data schemas, business rules, and cloud-based data solutions; monitors data loads and resolves availability issues. Collaborates with business, engineering, and privacy teams to ensure compliant data handling. Evaluates emerging data technologies, supports innovation, and helps adopt modern architectures, DevOps practices, and AI-enabled workflows.