Top Hybrid Data & Analytics Jobs

Design, build, and maintain data pipelines, ingestion frameworks, and ETL processes. Ensure data quality, manage warehouses/lakes (Snowflake, Databricks), enable data access via APIs/views, collaborate on data sourcing and governance, support analytics and ML deployments, and participate in migrations and code reviews.

Design, build, and maintain scalable data architectures and pipelines; ensure data quality, lineage, and observability; implement solutions across cloud and on-prem platforms; provide accessible data consumption methods; collaborate cross-functionally and exercise independent technical judgment.

Design, build, and maintain data structures and pipelines to ensure high-quality ingestion, transformation, and storage. Implement ingestion frameworks, data quality checks, lineage, and consumption layers (views/APIs). Deploy solutions on platforms like Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate cross-functionally, and contribute to internal tooling and architecture decisions.

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

Lead strategy, architecture, and execution of Comcast's scalable, secure data platforms. Drive cloud-native platform adoption, data governance, high-performance egress for BI, team building, cross-functional alignment, and operational excellence to enable analytics, ML, and self-service data consumption.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead development and analysis of a scenario-based market model to inform pricing, marketing, planning, and forecasting. Translate internal and external data into actionable consumer insights, present recommendations to senior leaders, manage supplier partners, drive platform enhancements, and collaborate across functions to integrate customer priorities into vehicle development.

Lead analytics projects supporting Rental and BCP Growth by partnering with dealers and cross-functional teams to analyze enterprise and dealer datasets, develop Power BI/SQL-based tools, identify trends and discrepancies, and translate findings into actionable recommendations for leadership to drive business performance.

Design, develop, and maintain Python data pipelines and CI/CD deployments to build business data objects. Implement source-to-target mapping as pipeline code, write unit and integration tests, build engineering automations, and resolve complex application issues. Collaborate cross-functionally, apply GenAI tools to improve developer productivity, and work with AWS services and Snowflake to support data-driven applications.

The Analytics Engineer will build data infrastructures, create dashboards, define metrics, structure datasets for AI projects, and foster insights across company functions.

As an Analytics Engineer, you will build dashboards, structure data, and prepare metrics for AI features while collaborating across teams to derive insights.

The Analytics Engineer will build data and analytics capabilities, create dashboards, partner with teams to define metrics, and prepare datasets for AI features.

Build and maintain customer-facing dashboards and reports, translate dealer requirements into actionable insights, interrogate underlying data models and pipelines, use AI to prototype analytics, and collaborate with engineering and product teams on data/platform migrations.

Build and maintain customer-facing dashboards and reports for automotive dealers, translate dealer requirements into actionable data products, debug and document data models/pipelines, use AI tools for rapid prototyping, and collaborate with engineering/product teams on data/platform migrations.

Perform data-driven operational analysis and support for DealShield. Build and maintain data assets, write and optimize SQL queries, develop dbt-based data models and reporting frameworks, track KPIs, diagnose process gaps, and recommend improvements. Partner with cross-functional stakeholders to drive performance, support pricing and margin decisions, and promote adoption of analytics, automation, and AI-enabled tools.

As a Decision Analytics Consultant, you'll leverage RWD and RWE analytics to support pharmaceutical clients by generating insights and conducting analyses for evidence generation and HEOR studies.

Develop and apply advanced statistical models, design custom analyses (Python/R/Tableau/SAS/Excel), synthesize and present results to clients, manage client relationships, create deliverables, and mentor junior team members.

The Quantitative Methodologist Expert will design and deliver complex quantitative research solutions, applying advanced methodologies to address business questions and drive data-driven outcomes.

As a Senior Product Data Analyst, you will analyze payment data, define metrics, design experiments, and provide insights to enhance product decisions.

The Data Analyst will evaluate data flow, develop solutions, communicate findings, create data visualizations, and ensure timely project completion.