Top Data & Analytics Jobs

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

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.

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.

Work with customers and internal teams to resolve production issues, improve reliability, and build automation. Develop and maintain C# and SQL Server solutions, improve observability, and convert operational learnings into permanent improvements while providing occasional after-hours support.

Design, build, and operate cloud-native, domain-oriented data products and pipelines across Azure and AWS using Databricks and Snowflake. Lead migration from legacy platforms, implement batch and streaming processing with Spark, ensure security and governance compliance, promote best practices (CI/CD, testing, observability), and mentor junior engineers while collaborating with product, analytics, and security teams.

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.

Design, build, and maintain scalable data stores, ETL/ELT pipelines, and data platforms (Databricks) to support analytics, reporting, and ML. Ensure data quality, security, governance, and collaborate with cross-functional teams on large, structured and unstructured datasets.

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.

Design, build, test, and operationalize complex data pipelines and solutions to support analytics, AI/ML, and BI. Analyze source data, apply transformations and data quality controls, troubleshoot defects, collaborate across teams, and educate end users on data products.

Builds, manages, and optimizes data ingestion, storage, integration, and processing workflows. Develops complex transformations and data models, ensures data quality, governance, metadata, and security, monitors pipelines, troubleshoots issues, and collaborates with product, IT, and business teams to deliver analytic solutions.

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.

Design, build, and maintain scalable AWS data pipelines and data lakes. Migrate legacy data to AWS, implement ETL/ELT, optimize storage and query performance, and enforce data quality, governance, and monitoring while collaborating with DBAs, data scientists, and stakeholders.

Design, develop, and maintain databases, reports, dashboards, and analytical tools. Collaborate with stakeholders to gather requirements, support DBMS administration, implement reporting solutions, and produce technical documentation to improve decision-making across County departments.

Design, build, and maintain scalable data pipelines and analytical infrastructure using AWS and modern data stack (Snowflake, dbt). Collaborate with product, engineering, BI, and data science teams to automate reporting, optimize storage and performance, implement ETL/ELT processes, ensure data security, and support BI platforms and CI/CD deployments.

Design, build, and maintain cloud-based ETL pipelines and data models for finance. Implement data security (RLS), semantic layers for Power BI, monitor data integrity, support analytics/AI initiatives, and collaborate with Finance, Accounting, and IT to deliver high-quality BI solutions.

Designs, builds, and maintains BI and data engineering solutions including ETL pipelines, Snowflake-based architectures, and BI tool integrations (Power BI, Business Objects, Tableau). Supports data modeling, database administration, ad hoc analysis, data governance, testing, monitoring, and platform modernization while collaborating with business teams and mentoring peers at senior levels.

Design, build, and scale high-volume data pipelines and architectures. Lead end-to-end data projects, improve feature engineering and models, ensure data quality and security, perform code reviews, and collaborate with data scientists and business stakeholders to deliver actionable insights.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.