Top Data & Analytics Jobs

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

As a Junior Data Engineer, you will assist in designing ETL/ELT workflows, maintain Python and SQL codebases, debug data pipelines, document processes, and collaborate with product teams on technical solutions.

The Data Engineer will build and maintain data platforms, deploy pipelines, monitor transformations, and collaborate with Data Scientists and DevOps.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

The Principal Data Engineer will lead the design of data frameworks, mentor teams, implement MDM solutions, and ensure data quality across environments.

The role involves migrating on-premises databases to AWS, developing Python scripts for data processing, and building AWS data pipelines. Responsibilities include supporting application teams, data modeling, and ensuring system efficiency.

The Data Engineer II will design and implement scalable data pipelines, collaborate with stakeholders, ensure data quality, and support analytics by utilizing cloud platforms and big data technologies.

The Data Engineer designs data architectures and pipelines, supports analytics teams, solves complex data challenges, evaluates tools, and mentors team members.

Lead design, build, and maintain scalable data pipelines and infrastructure using Azure Databricks, ADF, and SQL Server. Develop OLTP and dimensional models, integrate Salesforce and SaaS sources, implement batch and real-time processing, enforce data governance and CI/CD, troubleshoot performance, mentor team members, and drive improvements to reduce technical debt and improve cost and performance.

Build, operate, and optimize data platforms and pipelines (Snowflake, Azure ESB). Manage ETL, data migrations, ingestion, quality, and analytics tools (Cognos, Tableau). Collaborate with IT and business stakeholders, enforce data governance, support regulatory and cybersecurity requirements, and provide advanced support and system integration for banking APIs.

Design, build, operate, and support ETL/ELT pipelines, integrations, and curated datasets across enterprise systems (Salesforce, Deltek, Autodesk, Azure, Microsoft Fabric). Implement data models, orchestration, API-first services, data quality, governance, and monitoring to support BI, reporting, AI/ML, and secure data delivery.

Lead architecture and delivery of SoFi's data platform: build scalable data pipelines, drive cloud-native and AI-enabled tooling, enforce governance and security, mentor senior engineers, and support production on-call.

Design, develop, and implement complex cloud ETL processes for healthcare data. Support ETL operations (automation, scheduling, monitoring, maintenance, security), use AWS and Snowflake, employ Python and SQL, follow Agile and CI/CD practices, and identify process improvements to meet business and system requirements.

The Data Engineer will design, develop, and deploy scalable AI-enabled data solutions, implement machine learning models, and conduct data analysis in the DoD's CDAO ADA IR program.

The Data Engineer III designs and implements data pipelines and storage solutions, ensuring data quality for enterprise decision-making and collaboration with various teams to enhance data governance and architecture.

The Data Engineer I will build and maintain data pipelines, contribute to platform code, and integrate data sources while growing alongside senior engineers in a production-focused role.

As a Data Engineer, you will optimize data pipelines, develop code in Python and Spark, manage cloud infrastructure, and drive data solutions.

As a Data Engineer at SpaceX, you'll develop metrics and tools to assess the scaling and efficiency of the Starlink network, collaborating with various teams.

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

Lead design and execution of advanced analytical projects; architect and oversee scalable data pipelines and analytic solutions; develop and validate mathematical algorithms and models; build dashboards and visualizations; integrate analytics into operational workflows; mentor and direct technical teams; identify new analytic capabilities and system enhancements to improve customer experience and decision support.

Design, build, and scale cloud-native geospatial data products and processing workflows using Earth observation data to deliver field- and regional-scale insights for digital agriculture. Collaborate with data scientists, agronomists, and product teams to operationalize satellite-derived analytics and support machine learning workflows.

Design, build, and maintain scalable data pipelines and platforms. Collaborate with analysts, engineers, and stakeholders to ingest, process, and visualize structured and unstructured data. Support ETL, data integration, automation, and deployment of analytics workloads to enable mission-driven insights.

Support a Department of State data engineering program: review data policies and governance, define requirements, build and maintain Palantir-based data pipelines, clean and integrate datasets, detect and remediate data fidelity and pipeline issues, produce analyses and visualizations, and coordinate with non-technical users and stakeholders.

Builds and operates CBP data pipelines: configures Airflow/Glue/Spark/Kafka flows, performs ETL/schema mapping, runs quality checks, applies security/IAM patterns, triages ingestion failures, updates documentation, and tracks data pipeline performance to support analytics and mission partners.

Build and operate GCP-native data pipelines and lakehouse infrastructure using Airflow, dbt, and BigQuery. Design layered data models, manage ingestion (batch and streaming), implement observability and governance, and maintain CI/CD workflows to deliver reliable analytics for MLB stakeholders.