Top Data & Analytics Jobs

Supports GIS data creation, editing, cleanup, maintenance, QA/QC, standardization, and map product development for NASA Johnson Space Center facilities and utilities. Collaborates with field surveyors and external parties to transform and integrate data, creates story maps and dashboards, and edits data in a branch version environment. This temporary role requires three years of GIS experience, a relevant bachelor’s degree or equivalent experience, and U.S. continental location.

Develop and maintain manufacturing data infrastructure, databases, dashboards, and analytical tools integrating fab, equipment, metrology, test, yield, and reliability data. Build Python and JMP workflows, automate reporting and alerts, ensure data traceability and quality, troubleshoot dashboard and data-connection issues, and support SPC, DOE, process capability, reliability, and root-cause analyses. Collaborate with engineering, manufacturing, quality, reliability, and IT teams.

Leads the customer master data governance program and serves as business owner for the Informatica MDM platform. Oversees governance policies, data quality, customer hierarchies, stewardship operations, source onboarding, platform enhancements, and AI-enabled matching and deduplication. Partners with business, IT, technical teams, and external providers to define requirements, manage priorities, resolve issues, and improve data processes. Performs hands-on testing, validation, reconciliation, and user acceptance activities before platform changes are deployed.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Build and maintain a governed analytics platform in Snowflake using dbt, including dimensional models, data transformations, tests, contracts, semantic layers, and migrated reporting. Integrate diverse operational sources, ensure data quality, document models, and collaborate through GitHub pull requests. Use AI coding tools responsibly while validating correctness, performance, and maintainability.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Owns enterprise data architecture and scalable Databricks pipelines using medallion architecture and lakehouse formats. Designs dimensional models, star schemas, data marts, and the semantic layer; governs data quality and reliability; optimizes infrastructure and cloud costs; prioritizes deployments; collaborates with stakeholders; and mentors a junior data engineer.

Build and own marketing data, attribution, tracking, audience sync, and conversion infrastructure. Integrate product, CRM, warehouse, and advertising platforms; send offline conversions and LTV signals to ad platforms; maintain event schemas and dashboards; debug data discrepancies; implement privacy-safe tracking; and partner cross-functionally with growth, product, marketing, and data teams.

Build and operate production data pipelines that ingest, transform, validate, and deliver messy clinical data for machine learning, analytics, and clinician-facing APIs. Own data contracts, monitoring, backfills, failure recovery, and troubleshooting across systems. Collaborate with integration, ML, and product engineering teams to ensure reliable data delivery across hospital deployments.

Designs, develops, and maintains large-scale data processing systems, including ETL processes, data models, scalable pipelines, databases, data warehouses, and cloud-based big data solutions. Uses Snowflake, Fivetran, dbt, and Airflow while applying DevOps and security practices. Optimizes distributed processing and storage systems, collaborates with data scientists and analysts, and mentors junior engineers.