Top Senior Data & Analytics Jobs in Columbus, OH

Leads the full digital experience research lifecycle, including user interviews, testing, surveys, behavioral analysis, experimentation, and conversion optimization. Defines research and web measurement strategies, analyzes qualitative and quantitative data, builds dashboards and executive presentations, and translates findings into compelling data stories. Partners with marketing, technology, design, product, and development teams to improve global web experiences, while maintaining scalable research documentation and insight repositories.

Leads business and systems analysis for complex mortgage lifecycle initiatives, translating stakeholder needs into business, functional, data, API, integration, and nonfunctional requirements. Develops process and system models, user stories, acceptance criteria, and testing strategies; supports Agile delivery, releases, defect resolution, and post-production validation. Partners with Product, Engineering, QA, and business teams to manage dependencies, risks, and impacts across web, mobile, legacy, and integrated systems. Mentors Business Analysts and improves analysis standards and practices.

Build trusted GTM business intelligence by defining commercial metrics, analyzing performance across Sales, Marketing, Customer Success, Product, and Finance, diagnosing root causes, and delivering executive recommendations. Partner with data engineering and operations teams to integrate Salesforce, product, support, marketing, billing, and financial data. Evaluate AI-powered analytics capabilities and improve self-service decision-making.

Design, build, optimize, and operate enterprise cloud data platforms and Databricks ETL/ELT pipelines. Develop batch and streaming pipelines, data models, infrastructure-as-code solutions, BI dashboards, monitoring, and scalable ML models. Ensure platform reliability, governance, security, and compliance while collaborating with software, systems, product, and business teams.

Staff Data Analyst leading complex, cross-functional analytics initiatives and executive-level decision support. Responsibilities include defining metrics, building scalable data models and infrastructure with dbt, ensuring data quality and governance, developing analytical roadmaps, mentoring analysts, improving data literacy, and advising Sales, Marketing, Finance, and Operations stakeholders.

Owns the end-to-end data platform, including Snowflake infrastructure, ingestion pipelines, Airflow orchestration, dbt transformations, governed gold-layer models, and metric definitions. Ensures reliability, performance, testing, documentation, governance, and CI/CD standards. Builds data systems from scratch, partners with analytics stakeholders, and uses AI-assisted development tools to accelerate engineering work. Requires strong SQL, platform ownership, software engineering practices, and experience with Snowflake, Airflow, and dbt.

Build network capacity and demand forecasts across 50 states, support clinician recruitment and retention analytics, measure clinical and scheduling outcomes, and design experiments with Product and Engineering. Develop governed data models, KPI definitions, and AI-powered analytics infrastructure. Serve as the primary analytics partner for Network leadership by translating complex, multi-source data into actionable recommendations and presenting insights to technical and executive audiences.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Leads insight development, data analysis, reporting, and storytelling for consumer and telecom market research projects. The role manages client relationships, guides research design, presents actionable findings, creates client-ready deliverables, and provides consultative recommendations. It also leads project insight teams, supports proposals, coordinates with project management and sales teams, and manages multiple concurrent client engagements while ensuring analytical quality and process integrity.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.