Top Data & Analytics Jobs

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Build and maintain analytics-ready datasets and semantic models in Microsoft Fabric using SQL, dimensional modeling, DAX, and Power Query. Validate data quality, standardize metrics, support BI reporting, improve dataset usability, and align models with governance and architectural standards. Collaborate with BI developers, ETL engineers, business stakeholders, and data governance teams to deliver trusted, reusable analytical assets.

Leads Criminal and Housing Data Acquisition teams, overseeing vendors, data quality, compliance, staffing, KPIs, audits, training, reporting, and process improvement. Resolves escalated data issues, expands public-record coverage, and provides operational guidance while partnering with the Director of Data Acquisition. The role requires SQL Server, XML, HTML, reporting, criminal-record lifecycle, and team-supervision experience. Work is remote within the United States or office-based in Cleveland, with occasional overnight travel.

Analyze campaign performance across Google Ads, Meta, LinkedIn, and internal systems; diagnose underperforming campaigns; build Excel and Sheets models; audit campaigns across many locations; identify performance patterns; clean and analyze data; and help implement AI-enabled analytical workflows. The role emphasizes quantitative reasoning, basic code literacy, clear communication, and independent, detail-oriented work.

Designs and maintains scalable ETL pipelines for structured and unstructured data, supporting DEA legacy-data migration, validation, integration, and modernization. The role improves data quality, interoperability, reliability, and cloud-platform performance while preparing data for analytics and AI/ML use cases. Responsibilities include source-to-target mapping, troubleshooting, documentation, stakeholder collaboration, and participation in Agile development.

Supports data-driven talent decisions through workforce analytics, employee listening, dashboards, surveys, benchmarking, skills architecture, and talent measurement. Analyzes workforce trends involving retention, turnover, mobility, hiring, readiness, and capability; develops recommendations for HR partners and business leaders; supports workforce and succession planning, learning evaluation, talent acquisition metrics, and program effectiveness measurement. Maintains data integrity across talent systems and reporting processes.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Develops advanced analytics, econometric, forecasting, optimization, pricing, promotion, marketing mix, experimentation, and machine learning models to drive commercial growth and business decisions. Partners with cross-functional teams and external data providers to analyze complex datasets, ensure data quality, quantify business impact, and present executive-ready recommendations. Requires strong technical, business, communication, and stakeholder-influence skills.

Designs and owns production data schemas and models, builds AWS data infrastructure and artifact storage, and develops edge-to-cloud pipelines for robotic systems. The role also delivers ML infrastructure for training pipelines, dataset versioning, and model deployment. Candidates must have at least two years of AWS data infrastructure experience, strong distributed-systems modeling skills, ML infrastructure experience, and the ability to work across the stack. Travel to customer sites is required. Robotics, autonomy, industrial IoT, edge computing, and manufacturing experience are preferred.

Lead media research, campaign measurement, and analytics supporting sales, marketing, product, and client leadership. Develop audience analyses, benchmarks, case studies, surveys, and market insights; manage measurement vendors; shape advertising measurement capabilities; and serve as the senior client contact for research discussions. Establish analytical rigor, validate methodologies, document assumptions, and design AI-enabled workflows for client measurement challenges.