Top Data & Analytics Jobs in Cleveland, OH

Analyze business and data requirements, develop and optimize ETL processes, and support data integration, validation, reconciliation, business intelligence, and reporting. Ensure data quality across multiple sources, identify risks and improvement opportunities, collaborate with stakeholders and technical teams, and maintain documentation for data flows, mappings, and business rules.

Designs and leads Security Master, Entity Master, and Reference Data MDM architecture. Responsibilities include data modeling, governance, golden records, match-merge and survivorship workflows, ingestion pipelines, APIs, data quality, lineage, AWS infrastructure, and integration patterns. The role produces architecture documents, leads design reviews, collaborates with technical teams and stakeholders, recommends MDM patterns, and mentors backend and data engineering teams.

Supports Power BI reporting, Microsoft Fabric data organization, data validation, KPI tracking, recurring reports, and ad hoc analysis. Assists with data quality improvements, documentation, automation using Power Automate, and early AI initiatives. Collaborates with business and technical teams across Finance, Operations, Supply Chain, and Project Management while helping test and implement analytics solutions.

Designs and develops system-level workforce intelligence across the employee lifecycle. Identifies workforce trends, integrates HR insights with finance and operational metrics, creates executive-ready reporting and narratives, and supports strategic planning. Partners with data product owners and cross-functional stakeholders to align workforce intelligence with Federal Reserve System priorities and HR product strategy.

Designs, develops, and maintains Tableau dashboards, scorecards, and reporting solutions for compliance risk monitoring, control testing, regulatory reporting, and executive decision-making. Analyzes complex datasets, builds data models, validates data quality, connects multiple data sources, automates recurring reports, and supports audits and regulatory examinations. Collaborates with risk, audit, technology, and business teams while developing knowledge of banking products, source systems, regulations, and compliance frameworks.

Supervise actuarial predictive modeling initiatives for insurance pricing, portfolio management, and planning. Collaborate with actuaries, data scientists, underwriters, and management to develop, validate, govern, and implement models. Establish assumptions, loss adjustments, usage guardrails, monitoring practices, and model documentation. Support regulatory filings, improve team workflows, contribute to strategic projects, and mentor staff in actuarial and technical skills.

Analyze underwriting and claims data to set rates, prepare statistical analyses and filings, project loss trends, support product development, and present findings to stakeholders to inform pricing strategy.

Build and maintain data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, collaborate with analysts and testing teams, troubleshoot data issues, and develop machine learning and regression analysis capabilities using Azure ML and related tools.

Designs, builds, and maintains data pipelines, ETL processes, integrations, data models, schemas, and warehouse systems. The role optimizes data infrastructure for performance and scalability, implements governance, security, validation, and cleansing practices, and provides technical leadership to data engineering teams. It collaborates with clients, stakeholders, data scientists, analysts, and cross-functional teams to deliver reliable data solutions and evaluate emerging technologies.

Leads Northwest Bank’s enterprise data governance program, including data policies, quality rules, lifecycle management, metadata, master data, stewardship, governance tooling, and remediation. Facilitates Data Working Groups, investigates root causes, manages governance metrics and reporting, demonstrates tools, documents processes, and supports training. Partners across a matrixed organization to ensure data assets meet standards, controls, regulatory requirements, and business needs.

Conduct market, competitive, customer, product, industry, and performance analysis to support data-driven decisions across Lifecycle Services. Develop market insights and plans, guide go-to-market activities, monitor competitors, build cross-functional data-sharing programs, and support voice-of-customer, partner, and employee research. Collaborate with product management, marketing, sales, and global teams while using AI-based tools and quantitative and qualitative research methods.