Top Remote Data & Analytics Jobs in Tucson, AZ

Lead data product strategy and governance for customer-facing reporting within Managed Services. Own reporting product roadmap, data quality, standards, delivery platforms (Redwood Insights, BI, Snowflake, Excel), implementation readiness, and cross-functional alignment to scale trusted, automated customer analytics and reduce manual reporting.

Lead analytics strategy for federal programs: perform statistical modeling, forecasting, and data mining; build dashboards and automated workflows; ensure NIST/FISMA compliance and data quality; present findings to government stakeholders; mentor junior analysts and manage analytics workstreams.

Design, build, and optimize Databricks-based data pipelines and Lakehouse architectures. Implement ETL/ELT, MLOps, model tracking with MLflow, performance tuning, monitoring, and data governance across cloud storage platforms. Collaborate with architects, data scientists, and clients to deliver scalable, secure AI/ML solutions.

The Technical Enablement & Documentation Lead is responsible for developing and maintaining product documentation, creating a certification program, and collaborating with teams to improve the knowledge experience for Flosum's users.

As a Curriculum Manager, you'll create and manage cloud education content, collaborating with experts, utilizing AI tools, and addressing curriculum gaps.

Build and maintain enterprise data platform and ETL/ELT pipelines, design and deploy data models, ensure data quality and governance, integrate third-party sources, and support analytics and AI initiatives across the organization.

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Ensure engineering teams build the right work by producing high-quality context, maintaining AI-driven documentation, writing and validating SQL analyses and recurring reports, prototyping proofs-of-concept, and capturing acceptance criteria, edge cases, and measurable outcomes.

Embed with a single enterprise client to build and edit SQL for audience building and campaigns, design scalable data models, clean and automate pipelines, investigate data discrepancies, and improve workflows to support fast weekly delivery cycles.