Top Data & Analytics Jobs

Lead design and deliver enterprise data governance: implement data models, metadata standards, data cataloging/lineage, governed ETL/ELT pipelines, data quality controls, and coach teams on AI-assisted engineering and secure practices.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

Design, develop, and maintain scalable enterprise ETL/ELT pipelines using Databricks, PySpark, Spark SQL, Delta Lake, Python, and SQL. Integrate and transform data across enterprise sources, optimize performance, implement data quality and reliability improvements, and manage workflows with Azure Data Factory or Airflow. Apply cloud, data modeling, warehousing, Git, CI/CD, and DevOps practices while collaborating with cross-functional teams to deliver modern data solutions.

Design and maintain scalable ETL pipelines for structured and unstructured data, support DEA legacy-data migration, and implement data cleaning, validation, transformation, and integration processes. Monitor pipeline reliability and data quality, document mappings and workflows, and collaborate with analysts, data scientists, developers, and stakeholders. Support data preparation for analytics and AI/ML use cases within Agile development teams.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Build and maintain scalable batch and real-time ETL processes, data warehouses, lakes, and marts. Develop Power BI and SSRS reports and dashboards, automate data workflows, optimize queries, support secure data exchanges, protect sensitive information, and improve data quality and system stability. The role requires SQL and relational database expertise, collaboration with business users and data teams, and a focus on reliable, maintainable solutions. This is an entry-level campus recruiting position for May 2027 offers.

Develops and optimizes distributed genomic data pipelines using Hadoop, Spark, Hive, Impala, SQL, and ETL workflows. Integrates genomic, laboratory, epidemiological, and public health datasets from external repositories, ensuring data quality, governance, security, and reproducibility. Collaborates with bioinformaticians, epidemiologists, and laboratory scientists to support pathogen genomics, outbreak detection, surveillance, and situational awareness. Maintains version-controlled pipelines, documentation, data lineage, reports, and scientific communications. This is an on-site role in Atlanta with weekly team meetings.

Architects, deploys, and operates large-scale Apache Kafka and Confluent environments for mission-critical streaming workloads. Owns cluster sizing, configuration, security, automation, observability, high availability, disaster recovery, and developer enablement. Partners with application and cross-functional teams to build reliable streaming solutions, troubleshoot production issues, conduct design and code reviews, document systems, and mentor junior engineers.

Designs and enhances SQL Server databases, writes SQL and Transact-SQL procedures, manages ETL and ELT pipelines, troubleshoots data issues, optimizes database performance, monitors system health, and collaborates with engineers, data scientists, and business stakeholders to support scalable data flows and reporting.

Supports ETL pipeline development, legacy data migration, data transformation, validation, profiling, cleaning, and integration for the DEA’s investigative case modernization program. The role assists with source-to-target mapping, migration scripts, pipeline monitoring, troubleshooting, documentation, and Agile development activities while collaborating with technical and business stakeholders.

Build and maintain scalable data platforms, pipelines, and analytics solutions for mission-driven clients. Develop and deploy batch and streaming data pipelines, organize structured and unstructured data, and support platform assessment, design, development, and maintenance. Collaborate with analysts, developers, data scientists, infrastructure engineers, and data consumers in an agile environment while applying programming, database, automation, and cloud technologies.

Design, deploy, secure, and optimize enterprise database environments supporting mission-critical ArcGIS systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, data loading, and integration. The role operates across cloud, on-premises, and hybrid infrastructures and provides technical guidance to project teams and customers.

Design, develop, and maintain Python and SQL data pipelines, ETL workflows, database architectures, and AWS cloud integrations. Create schemas, stored procedures, documentation, testing strategies, and secure scalable data solutions. Troubleshoot data quality and performance issues, participate in Agile delivery using Jira, conduct code reviews, and guide other data engineers while collaborating with technical and program stakeholders.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Entry-level rotational Data Engineer role focused on SQL Server development, SSIS package creation, data validation, ETL, performance tuning, and cross-platform integrations (Salesforce, Marketing Cloud). Participate in SDLC, code reviews, data profiling, reporting, and stakeholder meetings with the goal of permanent placement after rotations.

Design, build, and maintain cloud-native AWS data platforms and scalable data lake/warehouse solutions. Develop and deploy data pipelines for structured and unstructured data to support AI/ML and BI, lead cross-functional teams, and manage cloud migration and modernization efforts. TS/SCI with polygraph and a CS bachelors degree required.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Support the design, development, and maintenance of data pipelines, workflows, and cloud-based data platforms. Build reliable data assets through ingestion, transformation, profiling, validation, monitoring, and data modeling. Collaborate with cross-functional teams to support analytics, reporting, automation, machine learning, and AI initiatives. Interns will gain experience with Python, SQL, ETL/ELT, Snowflake, cloud technologies, GitHub, and AI tools while receiving mentorship and professional development.

Data Engineer Interns support data pipelines, workflows, and platforms that enable analytics, reporting, automation, machine learning, and AI initiatives. Responsibilities include ingesting and transforming structured and unstructured data, applying data quality and validation practices, collaborating on scalable data solutions, and using Python, SQL, ETL processes, cloud technologies, and AI tools. The role includes mentorship, Agile exposure, and hands-on experience supporting data assets for insurance business needs.

Develops, tests, deploys, and maintains enterprise-facing data orchestration software that automates retail merchandising decisions. Collaborates with data scientists, product leaders, engineers, and business analysts to build cloud-deployed applications and data solutions. Participates in agile processes, follows engineering best practices, supports production applications, and continually develops technical and leadership skills.

Develop and maintain high-throughput backend and middleware systems for PlayStation’s cloud streaming data platform. Build Flink stream-processing and scalable ETL jobs, optimize data pipeline performance and reliability, support Kafka-to-cloud migrations, and contribute to architectural decisions. Analyze business requirements and collaborate with Business Intelligence and product teams to transform real-time and batch data into actionable insights.

Designs and maintains scalable enterprise data architectures and ETL/ELT pipelines on Microsoft Fabric and Azure. Responsibilities include developing Python-based data solutions, optimizing Fabric warehouses and lakehouses, implementing governance, security, lineage, monitoring, and CI/CD, and integrating diverse data sources. The role partners with data science, machine learning, business intelligence, engineering, and business teams to support analytics and model training while improving platform reliability, scalability, and cost efficiency.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.