Top Data & Analytics Jobs

Develop and optimize advanced SQL transformations, stored procedures, ETL workflows, and data-quality processes for large Medicaid claims and health-related social needs datasets. Build reproducible, documented data pipelines; calculate healthcare quality, equity, and disparity metrics; conduct code reviews; and collaborate with stakeholders to turn complex data into visualizations and policy insights.

Administer and optimize P21/SQL databases, design logical and physical data models, manage SSRS reports and SSIS packages, ensure security, backups, and recovery, tune performance, collaborate with cross-functional teams and vendors, and document database procedures and policies.

Validate and review CO APCD-derived data products using SQL and visualization tools, perform output comparisons and project-level analyses, follow quality control standards, document findings, and communicate results to technical and non-technical stakeholders while supporting continuous improvement of data QA processes.

Build and maintain scalable data models, pipelines, ETL workflows, and streaming solutions using dbt, PySpark, AWS Glue, Databricks, and Kafka. Apply software engineering practices including testing, version control, code reviews, and integration. Partner with data scientists, stakeholders, and data stewards to improve data quality, governance, accessibility, and reliability. Translate requirements into development plans and Jira stories while collaborating on data strategy, tooling, integrations, and deployments.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Lead strategy, architecture, and delivery of enterprise data engineering on the Data Cloud Platform. Design batch and real-time pipelines, event-driven streaming, data products, governance, lineage, quality, and observability. Provide technical leadership, mentor teams, drive adoption of standardized data models, CI/CD, and align platform capabilities with business and investment use cases.

Conduct quantitative research for algorithmic trading and market-making strategies. Design, test, deploy, and monitor models supporting pricing, risk, and execution. Build research and backtesting tools, analyze large datasets and model outputs, and collaborate with traders, developers, and senior researchers to translate hypotheses into production trading decisions. Continuously improve strategies based on market dynamics, data drift, and performance results.

Leads an eight-person analytics engineering team and sets strategy for trusted semantic models, governed data, enterprise BI, AI-powered data access, and production data applications. Oversees data modeling, evaluation datasets, telemetry, LLM routing, governance, security, and self-service capabilities. Partners cross-functionally to establish engineering standards, roadmaps, and measurable business impact while hiring, coaching, and developing team members.

Lead the redesign and implementation of a scalable Azure and Databricks lakehouse. Responsibilities include migrating Azure Data Factory pipelines, building Delta Lake architecture, developing SQL and Python transformations, establishing engineering standards, monitoring data quality, partnering with stakeholders, leading technical projects, mentoring engineers, and supporting future team growth.

Develop data models, analyses, dashboards, and automated reporting for a collectibles marketplace. Partner with engineering, product, and data platform teams to define business metrics, identify customer experience and growth opportunities, inform strategy, and lead initiatives that improve business performance.