Top Hybrid Data & Analytics Jobs

Leads the People data ecosystem, including Snowflake workforce data models, pipelines, governance, metric certification, and data quality. Develops scalable workforce analytics products using Visier and Snowflake, enables governed AI-driven insights, replaces manual reporting with self-service solutions, and manages a high-performing team of data and analytics professionals.

Analyze, clean, validate, and reconcile data to produce dashboards, reports, metrics, SQL queries, analytical datasets, and business insights. Partner with stakeholders to define requirements, investigate trends and anomalies, validate data models and workflows, improve data quality, and establish scalable analytics standards and documentation.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, segmentation, and LTV analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and deliver actionable recommendations. Requires strong SQL and Python skills, causal measurement expertise, marketing platform experience, and the ability to communicate insights to senior stakeholders.

Build trusted, secure, and repeatable data pipelines, transformations, data products, retrieval layers, and semantic data assets. Assess data quality, lineage, governance, privacy, and access constraints; support AI retrieval, embeddings, vector stores, and grounding strategies. Create test and de-identified datasets, implement monitoring and quality checks, document data assets, and ensure reliable data for AI products, control-group testing, and production handoff.

Build and maintain scalable data pipelines supporting reporting, analytics, and AI/ML use cases. Write production Python and SQL code, integrate data from APIs and other sources, establish data governance, implement quality monitoring and alerting, support production incidents, and document technical solutions. Collaborate with data scientists, engineers, and business partners to deliver reliable data infrastructure and insights.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Leads the acquisition, integration, modeling, and development of data analytics products. Designs data lake and warehouse solutions, builds ETL pipelines, applies engineering and CI/CD practices, supports analytics and machine learning applications, and ensures data quality and regulatory compliance. Collaborates with technical and business stakeholders, contributes to agile delivery, leads code reviews, mentors junior engineers, and develops enterprise data flows that establish a reliable source of truth.

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.