Top Data & Analytics Jobs

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.

The role involves managing the data infrastructure at Convo, optimizing ETL/ELT processes, ensuring data quality, and collaborating with cross-functional teams on data projects.

Manage delivery of complex data pipelines in a hands-on role, leading a team of data engineers to develop scalable data solutions and improve analytics capabilities.

As a Data Engineer at Booz Allen, you'll design and develop data pipelines, support data systems, and ensure data security while working in a team-focused environment.

The Forward Deployed Data Engineer at ZoomInfo designs and delivers custom intelligence applications for strategic accounts, managing engagements end-to-end and collaborating with various teams to optimize data solutions.

Support the creation of business intelligence tools for the War Research Enterprise by developing scalable data pipelines and governance policies while collaborating with cross-functional teams.

Design, build, and maintain scalable batch and near-real-time data pipelines and data products using Databricks and Spark. Integrate internal/external sources, implement data models, ensure data quality, support BI (Sisense), CI/CD, and collaborate with analysts, scientists, and stakeholders to enable analytics and decision-making.

Own and scale ReadyOn's data platform: design/build real-time and batch pipelines, implement ingestion and transformation workflows, model schemas in Snowflake and PostgreSQL, ensure observability and data quality, build connectors and feature-store roadmap, and provide technical leadership and mentorship across data, AI, and backend teams.

Support and maintain data ingestion, ETL, and normalization pipelines into Azure SQL and Azure Data Lake using ADF, Databricks, SQL Data Tools, and Python. Ensure data quality and security, assist client onboarding, create technical database documentation, and collaborate with data and client services teams to support higher-education partners.

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

Senior ETL Data Developer to design, build, test, and support Epic Clarity and enterprise ETL solutions using SSIS (and DataStage). Develop and optimize ETL workflows, implement standards, troubleshoot complex integration issues, document designs, and collaborate with reporting, analytics, and business teams to deliver data warehouse solutions and integrations.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

The Data Engineer I at Storable supports the design and maintenance of data pipelines, works with ETL processes, optimizes workflows, and collaborates with various teams to enhance data accessibility and reliability.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

Design, build, and manage large-scale data structures, pipelines, and ETL workflows. Integrate data for analytics and Data Science, optimize Hadoop/HDFS processes, develop data marts and models, ensure data quality and accessibility, and evaluate/adopt tooling for real-time and offline processing.

Design, build, and maintain large-scale data structures, ETL/ELT pipelines, and data marts. Integrate data from multiple sources, ensure data quality, and enable analytics and ML model integration. Leverage Hadoop/HDFS, cloud platforms, Airflow, streaming, containers, and generative AI frameworks to deliver scalable, auditable pipelines and backend services.