Data Product Analyst
EXL
Data Product Analyst
EXL
Analyzes business data and runs outsourced operations
The Data Product Analyst serves as a critical bridge between business stakeholders and data engineering for the international data platform. This role combines strong SQL and technical depth with core business analysis skills to ensure that data is trustworthy, accurately specified, and effectively leveraged to inform decision making. The Data Product Analyst owns the path from business request to trusted data in production: eliciting complete requirements, following the work through build and release, and monitoring the data once it is live. Over time, the role becomes the subject matter expert for international data.
Bachelor's degree in information systems, computer science, data analytics, or a related field, or equivalent experience.
5+ years of experience in data analysis, product analytics, analytics engineering, or similar roles, with extensive hands-on SQL experience.
Proven ability to work effectively in cross functional environments and support the delivery of data products that serve multiple stakeholders.
Demonstrated record of ownership: following requests through delivery and production and resolving issues across business and engineering teams.
Advanced SQL and analytical toolkit (with Python proficiency) used to explore, transform, and validate complex datasets across multiple product disciplines, including tracing data lineage and verifying data integrity without engineering dependencies.
Solid technical background in data modeling, ETL/ELT pipelines, and cloud data platforms (e.g., AWS), with enough depth to understand how data products are built and to partner credibly with engineers.
Skilled at requirements elicitation and translating business needs into technical specifications.
Skilled in crafting user stories, acceptance criteria, and high-quality product and data documentation to support scalable, repeatable workflows.
Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use and the ability to adapt analytical and stewardship skills across a wide range of use cases.
Comfortable connecting data across multiple systems and enriching datasets to enable new insights for business stakeholders.
Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders.
Collaborative mindset with the ability to influence without authority and drive alignment across cross functional teams.
Thrives in ambiguity by asking clarifying questions, exploring new data sources, and uncovering insights that drive team action and improve data quality.
Strong critical thinking and structured problem-solving, especially when interpreting incomplete, messy, or evolving datasets.
Act as the Data Subject Matter Expert to stakeholders, building deep knowledge of international data across the end‑to‑end data supply chain through hands-on work on every intake and production incident
Make international data trustworthy by working directly with business and marketing stakeholders to resolve data issues and build their confidence in the data.
Query, trace, and validate data from multiple sources to investigate anomalies, answer ad hoc requests, and uncover trends, patterns, and actionable insights.
Translate analytical findings into clear, business friendly insights that support decision‑making. Go beyond producing numbers: state what they mean in a sentence and anticipate the stakeholder's next question.
Elicit, document, and manage business, functional, and nonfunctional requirements through techniques such as interviews, workshops, process analysis, and user story creation. Write complete intakes that capture every requirement in as much detail as possible.
Own delivery after handoff: check in regularly with data engineering, confirm that what is built matches what was requested, and drive issues to resolution without waiting for direction.
Support prioritization by assessing business value, impacts, complexity, and risk.
Ensure traceability of requirements from intake through development, testing, and deployment.
Collaborate with data engineering analysts, architects, and engineers to ensure solutions reflect business context and use cases.
Define acceptance and UAT criteria that go beyond what the business specifies, including data accuracy, completeness, and edge cases; support user validation and provide structured feedback for continuous improvement.
Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities.
Build and maintain business-rule monitors and alerts that catch business logic issues engineering's technical checks may miss, and review past incidents to close monitoring gaps.
Participate in PSG and release go/no-go meetings to stay informed on production changes that may affect data quality or reporting.
Manage time and availability: decide which meetings need active participation versus a recording and raise calendar overload or availability issues early.
EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.
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