Top Data & Analytics Jobs

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

As a Business Intelligence/Data Engineer, you will analyze data structures, implement data warehousing solutions, and design data visualizations while working with AI/ML services in cloud environments.

The Principal Data Engineer will lead data architecture initiatives, design scalable data systems, and oversee data standards across teams while ensuring high-quality data infrastructure and guiding engineering teams.

The IT Controls Data Engineer will create and maintain data pipelines and automated evidence workflows for IT controls, ensuring compliance and audit readiness.

The role involves managing data infrastructure, building data pipelines, providing analytics to inform business decisions, and shaping AI workflows in the organization.

The role involves designing and building scalable data platforms, migrating data to the cloud, and implementing CI/CD practices while collaborating with cross-functional teams.

Develop and design data pipelines, optimize data delivery and automate processes to support end-to-end solutions in various settings.

Design and maintain data pipelines and infrastructure, develop ETL processes, collaborate with stakeholders to integrate data, and optimize database performance.

The role involves structuring and ingesting customer data, setting up ETL pipelines, and providing solutions for data export and import.

The Data Engineer III will lead the development and maintenance of data infrastructure and tools, ensuring data integrity while driving strategic data initiatives.

As a Data Engineer, you'll design and optimize data pipelines, collaborate with teams for analytics and machine learning, and enforce data governance standards.

The Data Engineer will develop production-ready data models, collect business requirements, design data warehouses, and implement ETL best practices while collaborating with teams.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

Design, build, and maintain Master Data Management (MDM) data integrations and data quality flows. Define standards for scalability, security, error handling, and auditing. Estimate and scope implementations, produce technical specifications, implement DevOps for integrations, participate in testing, support offshore/third-party teams, and drive continuous improvement for MDM solutions.

Design, build, and maintain scalable data pipelines and models for Finance and operations; collaborate with analysts and data scientists to provision trusted analytics; ensure governance, performance, testing, and incident root-cause fixes; recommend technologies and mentor team members to adopt cloud-first, best-practice data architectures.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

Design, build, and maintain systems and architecture to collect, store, and analyze large volumes of data in support of DoD/IC programs. Work with stakeholders to enable data ingestion, storage, and analytics pipelines.

Lead implementation of ETL patterns and reliable data infrastructure, partner with business and engineering teams to prioritize and deliver integrations, APIs, and reusable components, and support operational data applications in an Agile environment.

Design, build, and maintain an internal data platform and pipelines. Ensure secure, governed, resilient data access for engineering and BI teams. Partner with architects, implement automations, support platform consumption, mentor users, and drive continuous improvement and compliance.

Design, build, and deploy scalable data pipelines and validation for government production environments. Establish and maintain schemas, optimize SQL, handle messy/disparate data sources, work with large (100GB+) workflows, use Databricks/Spark/Python, and support ML/predictive solutions while mentoring teammates and engaging with clients. Active Secret Clearance required.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

Lead and mentor engineering teams to design, build, test, and support cloud-based data platforms and streaming solutions. Implement big data, ETL, and data warehousing using cloud services and distributed tools, collaborate with product managers, and ensure high-quality, performant code.

Design and own the companys end-to-end data and analytics stack: build metrics, dashboards, and reporting; instrument product and GTM signals; transform messy data into structured inputs for AI agents; and define analytics infrastructure (dbt, Snowflake, Mode) while partnering across founders, engineering, and GTM teams.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Develop and maintain data infrastructure for safety data analysis, build scalable data pipelines, conduct exploratory analyses, and support regulatory reporting in AV safety.