Top Data & Analytics Jobs

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Designs, builds, and scales cloud-based batch and streaming data platforms and pipelines. Owns data workflow reliability, performance, testing, monitoring, and quality. Implements modern data stack solutions using dbt, Databricks, AWS, Snowflake, Airflow, Fivetran, Kafka, and Kinesis. Collaborates with cross-functional teams to translate business needs into scalable data solutions, while leading projects, influencing stakeholders, mentoring peers, and promoting engineering best practices.

Build and maintain reliable data ingestion, transformation, and modeling pipelines using Python, Dagster or Airflow, dbt, Snowflake, Airbyte, and AWS. Responsibilities include API and PostgreSQL extracts, Parquet and Snowflake loads, production monitoring, troubleshooting, backfills, incremental modeling, code reviews, Terraform updates, documentation, and runbooks. Partner with analytics and product teams on dimensional modeling, data quality, and pipeline reliability.

Develop and maintain ETL/ELT data pipelines using Databricks, PySpark, SQL, dbt, and Snowflake. Apply Snowflake performance tuning, Unity Catalog, data quality frameworks, and enterprise SaaS integrations including Salesforce, D365, Coupa, Workday, and Concur. Candidates should understand LLMs, RAGs, and AI agentic tools, with strong Python and SQL skills. The position requires 12 or more years of overall experience, U.S. citizenship, and local availability.

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.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

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, build, and maintain scalable Databricks data pipelines, lakehouse architectures, data models, and ETL/ELT workflows. Implement governance, security, PHI isolation, quality testing, metadata, lineage, monitoring, and CI/CD practices. Collaborate with architects, data scientists, analysts, and stakeholders to deliver reliable data solutions, integrate AI and analytical tools, troubleshoot performance issues, and support enterprise data platform improvements.

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, build, and maintain scalable data pipelines and platforms. Implement ETL/ELT workflows, parse and process structured/unstructured data, and support data platform scaling, monitoring, and operations while working on cross-functional agile teams.

The Data Engineer will develop and maintain data pipelines, perform backend integrations, and enhance data systems for effective reporting and analytics.

Design, build, and maintain end-to-end data pipelines and cloud infrastructure on GCP. Use SQL and Python for data modeling and analysis, orchestrate jobs with Airflow/Astronomer, provision infrastructure with Terraform and Tekton, and create Looker Studio dashboards to present insights to stakeholders.

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.

Lead design, build, and deployment of secure, scalable data pipelines and analytics platforms (including Palantir) in constrained or classified environments. Mentor multidisciplinary teams, integrate AI/ML and LLM capabilities into production workflows, implement DevSecOps practices, and communicate architectures to technical and nontechnical 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.