Top Data & Analytics Jobs

Design, build, and maintain cloud-based data pipelines and architectures on AWS, Azure, or GCP. Advise clients on cloud data solutions, implement production data products, collaborate with stakeholders, and evaluate architectural trade-offs to meet business needs.

Design, build, and maintain cloud data pipelines and data architecture on AWS/Azure/GCP. Advise clients on cloud-based ingestion and processing, collaborate with stakeholders, and deliver production-ready data products while evaluating architectural trade-offs.

Design and implement star-schema dimensional models and ETL pipelines, optimize SQL Server data operations, develop data processes and applications, deploy data solutions via CI/CD, and collaborate with clients to meet analytics and business requirements.

Analyze manufacturing, test, telemetry, warranty, field-return, and failure-analysis data to identify hardware quality trends and emerging issues. Build statistical analyses, dashboards, monitoring indicators, and automated reporting. Connect product performance to manufacturing processes, components, and test results; support failure investigations and corrective actions. Partner with hardware, manufacturing, quality, reliability, firmware, data engineering, and data science teams to resolve data gaps and communicate actionable recommendations.

Build and maintain production data pipelines and models using SQLMesh, BigQuery, and Python. Improve observability, reliability, and performance; support finance and revenue pipelines; participate in on-call rotation and own features end-to-end.

Design, build, and maintain data integration and ETL solutions to implement client hospital data into Meduit platforms. Develop Python scripts, SQL queries, and data models; ensure data quality, transform and map source systems, and support data warehouse and architecture improvements during client implementations.

Design, build, test, and support cloud-based data engineering solutions: ETL pipelines, data workflows, APIs, and data services. Deploy and troubleshoot in Linux/cloud environments, collaborate with platform and DevSecOps teams, ensure secure coding, document data flows, and support mission-driven programs.

Design, build, and maintain scalable data lakes, warehouses, and production ETL/ELT pipelines. Automate data pipelines, ensure data quality via testing, collaborate with architects, data scientists, and DevOps, integrate data solutions into client environments, and implement data recovery and security measures.

Design, build, and maintain scalable data solutions that extract, transform, and deliver Oracle EBS data for company-wide analytics and operations. Develop ETL using SQL/PLSQL and Java/Python, document technical processes, perform testing and troubleshooting, ensure data quality and observability, support multiple teams, provide on-call and after-hours support, and work onsite in Sarasota, FL.

Build and maintain data pipelines using Python and Spark/PySpark. Work with Presto/Athena and Airflow to collect, aggregate, and store data powering BI. Emphasizes strong algorithms, data structures, and shipping high-quality software.

Design, build, and optimize scalable cloud-based data pipelines using PySpark and Databricks. Develop and maintain ETL/ELT workflows, model data for warehousing in AWS Redshift, and tune performance. Troubleshoot production issues and work with Python and SQL to process large-scale distributed data. Prior domain experience in e-commerce, airlines, transportation, or logistics is desirable.

The Data Engineer/ETL Engineer will support cloud-based and on-prem application development, focusing on data management and analytics for defense and intelligence.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Build and scale optimized data pipelines and data engine architecture to run ML models, reduce annotation costs, integrate foundation models for labeling and QA, use software/hardware-in-the-loop testing, and collaborate with DoD customers to understand requirements and support field events.

Lead Data Engineer to design, develop, test, implement, and support cloud-based big data solutions. Collaborate across Agile teams on distributed, streaming, and full-stack systems using Java/Scala/Python, RDBMS and NoSQL, Redshift/Snowflake, and cloud platforms. Mentor engineers, perform code reviews, and ensure performant, well-tested implementations.

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

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

Design, build, test, deploy, and monitor batch and streaming data pipelines and ETL processes. Evaluate and adopt data technologies (including Adobe Experience Platform and CDPs), collaborate with data scientists, architects, and business partners to ingest and prepare large semi-structured/unstructured datasets for ML training, model execution, and search analytics, and implement automation, documentation, and tooling for scalable analytic data systems.

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

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.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

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

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