DataOps - Lead Data Analyst

The Cigna Group•Remote,

2d•$82,200 - $137,000•Remote

About The Position

The Lead Data Analyst is responsible for translating business opportunities, customer needs, and operational challenges into actionable data requirements, integrated data solutions, and analytical deliverables. This role partners directly with business customers, Digital Product, Analytics, AI, Data Operations, and Technology teams to understand business processes, document requirements, assess data, validate solutions, and ensure delivered capabilities solve the intended business problem. The Lead Data Analyst serves as a key bridge between business stakeholders and the DataOps Engineering organization, providing hands-on expertise across business analysis, data profiling, solution validation, customer enablement, and process improvement so data and engineering investments align to measurable business outcomes.

Requirements

5+ years of experience in data analysis, business analysis, data products, analytics consulting, or enterprise solution delivery.
Demonstrated experience gathering and translating business needs and operational processes into functional requirements, business rules, acceptance criteria, and data specifications.
Strong experience with data discovery, profiling, data quality assessment, testing, business validation, and solution adoption.
Proficiency with SQL, data analysis techniques, and enterprise analytical tools.
Experience working with cross-functional business, product, technology, and data teams.
Experience supporting business process automation, workflow solutions, or AI-enabled capabilities and translating business requirements into actionable solution designs.
Understanding of enterprise data platforms, integration patterns, analytics, AI, metadata, and automation technologies.
Strong customer engagement, communication, facilitation, problem-solving, and stakeholder management skills.
Experience in complex, regulated enterprises preferred.

Nice To Haves

Experience with Databricks or comparable cloud data platforms and data product development initiatives.
Experience with workflow automation platforms, low-code solutions, process mining, or AI-enabled business solutions.
Knowledge of metadata management, data governance practices, and enterprise data architecture concepts.
Experience in healthcare, insurance, or another highly regulated industry.

Responsibilities

Partner with business stakeholders to understand business objectives, operational processes, decision-making needs, and desired outcomes and how data supports them.
Facilitate discovery sessions, requirements workshops, and stakeholder interviews to translate customer needs into clear functional requirements, business rules, acceptance criteria, and value measures.
Ensure business context, intended use, process requirements, and expected outcomes are understood and documented before solution development begins.
Perform data discovery, profiling, and analysis to evaluate quality, completeness, metadata, usability, and fitness for purpose, identifying gaps and remediation needs early in the lifecycle.
Define and document business semantics, data definitions, transformation logic, source-to-target mappings, data relationships, and readiness criteria.
Develop clear functional and data requirements that enable efficient solution development.
Partner closely with Data Operations and Data Engineering throughout design and delivery to ensure solutions reflect business intent and support reuse.
Develop analytical datasets, reusable data assets, and Gold layer data solutions when business requirements can be effectively addressed through analyst-led solution development.
Develop and execute functional testing, test cases, and business acceptance validation for engineered datasets, data products, and integrated solutions.
Coordinate customer feedback, user acceptance testing, adoption, and transition activities to ensure solutions are integrated successfully into business workflows.
Assess business processes to identify opportunities for workflow improvement, process simplification, and operational efficiency gains.
Identify opportunities where data, workflow automation, AI, or integrated solutions can improve business processes, operational efficiency, and customer outcomes.
Partner with automation, AI, DataOps, and Technology teams to define requirements, validate solutions, and measure realized business value when automation is appropriate.
Develop analytical solutions, prototypes, and business insights when customer needs can be met effectively without extensive engineering investment.
Support rapid proof-of-concept and learning-stage work while establishing a clear pathway for solutions that require enterprise-scale operationalization.
Prepare documentation, training, and knowledge-transfer materials that enable customers to adopt and use data products, integrated solutions, and automated workflows effectively.
Use AI, metadata, workflow, and reusable analytical patterns to improve requirements discovery, data profiling, documentation, testing, and traceability.
Develop reusable templates, requirements assets, testing approaches, and documentation standards that increase consistency and delivery speed.
Contribute to repeatable operating practices that reduce manual effort and improve quality across the solution lifecycle.