Practice - AIA - Artificial Intelligence and Analytics
About AI & Analytics: Artificial intelligence (AI) and the data it collects and analyzes will soon sit at the core of all intelligent, human-centric businesses. By decoding customer needs, preferences, and behaviors, our clients can understand exactly what services, products, and experiences their consumers need. Within AI & Analytics, we work to design the future—a future in which trial-and-error business decisions have been replaced by informed choices and data-supported strategies.
By applying AI and data science, we help leading companies to prototype, refine, validate, and scale their AI and analytics products and delivery models. Cognizant’s AIA practice takes insights that are buried in data and provides businesses a clear way to transform how they source, interpret and consume their information. Our clients need flexible data structures and a streamlined data architecture that quickly turns data resources into informative, meaningful intelligence.
*Please note, this role is not able to offer visa transfer or sponsorship now or in the future*
Job Summary
The Business Data Analyst (Data Focus) is a senior individual contributor who combines strong technology analysis skills with deep data expertise to shape and deliver large, data-intensive projects and programs. This role partners with business leaders, product owners, and engineering teams to translate complex, multi-domain business outcomes into secure, scalable solution requirements for initiatives that are heavy in data movement, mapping, and modeling. The analyst understands data lifecycle end-to-end—across sourcing, transformation, curation, consumption, and run—and ensures requirements reflect how data flows, changes, and is governed across projects and platforms.
In this role, you will:
· Business & Data Outcomes Definition
o Partner with business leaders and product owners to understand strategic objectives, KPIs, and regulatory or financial reporting needs for large initiatives.
o Decompose complex business outcomes into data-centric questions and high-level solution capabilities (e.g., domains, subject areas, key entities and metrics).
o Identify cross-project and cross-platform data dependencies, constraints, and risks early in the lifecycle.
· Requirements for Data-Heavy Projects & Programs
o Lead the requirements lifecycle for large, multi-team efforts that involve significant data movement, mapping, and modeling (e.g., new data platforms, migrations, cross-domain integrations).
o Translate business outcomes into clear functional and non-functional requirements (performance, latency, freshness, lineage, SLAs) for data pipelines, models, and products.
o Own or co-own complex epics and feature sets, ensuring requirements are testable, traceable, and aligned to data quality and governance expectations.
· Data Mapping, Modeling & Lifecycle
o Drive end-to-end source–target mapping across multiple systems, including legacy, SaaS, and modern cloud platforms; document transformations, derivations, and standardization logic.
o Collaborate with data architects, data modelers, and data engineers to shape conceptual, logical, and physical models that support analytics, operational reporting, and AI use cases.
o Ensure requirements reflect the full data lifecycle (ingest → harmonize → curate → consume → archive/retire), including lineage, retention, controls, and downstream impact analysis.
· Secure, Reliable Data Solutioning
o Apply secure software and systems engineering practices throughout the delivery lifecycle to protect data and technology solutions from threats and vulnerabilities.
o Define and refine requirements for data quality rules, monitoring, and reconciliation (e.g., row counts, hash totals, key coverage, exception handling) that engineers and testers can automate.
o Partner with risk, governance, and architecture teams to ensure solutions meet Trusted Data, privacy, and regulatory standards.
· Facilitation, Documentation & Traceability
o Lead structured facilitation sessions (workshops, visual modeling, process/data flows, journey maps) with diverse stakeholders across business, technology, and data governance.
o Produce and maintain high-quality requirements artifacts (epics, features, user stories, acceptance criteria, data contracts, mapping specifications) and keep them current in team tools (e.g., Jira/ADO, Confluence).
o Maintain clear traceability from business outcomes to requirements, data elements, test cases, and production controls.
· Cross-Team Collaboration & Delivery Support
o Collaborate with software engineers, data engineers, quality engineers, architects, and operations teams to ensure shared understanding of data requirements and solution intent.
o Provide analytical input to estimation, sequencing, and dependency management for data-heavy work across multiple squads or vendors.
o Support solution validation by reviewing test strategies, test data needs, and defect triage for issues related to data, mapping, and modeling.
· Coaching, Standards & Continuous Improvement
o Mentor junior and mid-level analysts (E/F band) on data-centric analysis practices, mapping techniques, and documentation standards.
o Contribute to and help enforce common templates, patterns, and “golden paths” for requirements on data projects (e.g., standard mapping specs, lineage views, quality rule catalog).
o Identify and drive improvements in how teams capture data requirements, manage data lifecycle impacts, and prevent recurring data issues.
What you need to have to be considered
· Minimum 6–10+ years of relevant experience in Technology Analysis, Business Systems Analysis, or similar roles.
· Proven track record leading requirements for large, data-intensive projects and programs, including migrations, new data platforms, or cross-domain integrations.
· Hands-on experience with data-centric analysis: data movement, mapping, modeling, profiling, and lifecycle considerations across multiple systems and environments.
· Experience working in Agile or hybrid delivery models on multi-team efforts.
· Strong understanding of data concepts (entities, relationships, keys, domains), data modeling patterns (relational, dimensional, lakehouse), and data lifecycle across environments.
· Practical experience interpreting and specifying data flows, data contracts, and interface requirements between systems and platforms.
· Familiarity with SQL and/or other data analysis tools sufficient to validate assumptions, mappings, and edge cases
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Applications will be accepted until 27 May 2026.
Salary and Other Compensation:
The hourly salary for this position is between $[85,885 - 123,500] depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
· Medical/Dental/Vision/Life Insurance
· Paid holidays plus Paid Time Off
· Long-term/Short-term Disability
· Paid Parental Leave
About us
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, building the bridge between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, realize tangible returns and keep global enterprises ahead in a fast-changing world. See how at www.cognizant.com or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
