Gather and analyze business and technical requirements to develop impactful Data Solutions, leveraging both traditional discovery techniques and AI assisted insights.
Demonstrate expertise through a consultative and collaborative approach to engineering, working closely with data engineering, data science, and platform teams.
Apply strong problem solving skills grounded in a deep understanding of data architectures, pipelines, data warehouses, serving layers, and modern analytics patterns.
Document end to end data flows from source to target at a functional level, ensuring clarity, traceability, and alignment with business outcomes.
Investigate and perform deeper analysis to produce high impact data solutions that achieve targeted and measurable outcomes.
Perform quantitative analysis to identify, diagnose, and resolve data quality, performance, and reliability issues.
Work closely with customers to understand their questions and needs, then navigate data rich environments to uncover insights and complete their information puzzle.
Effectively communicate—both orally and in writing—with peers across Data Products, Enterprise Architecture, Technology, and Client Success teams.
Leverage Microsoft Copilot and AI assisted tooling across the product lifecycle, including requirements discovery, backlog refinement, technical documentation, data analysis, and stakeholder communication.
Use AI driven insights to accelerate problem discovery, hypothesis validation, prioritization, and decision making.
Partner with engineering and data science teams to embed AI assistance directly into data products, dashboards, and workflows, improving end user decision quality and efficiency.
Contribute to the design of agentic data systems, where autonomous or semi autonomous agents assist with data quality monitoring, anomaly detection, root cause analysis, pipeline orchestration, and operational observability.
Support the evolution from traditional dashboards to AI powered, conversational, and goal oriented data experiences that adapt to user context and intent.
Help define scalable product patterns where intelligent agents proactively surface insights, recommend actions, and continuously learn from usage signals.
Apply modern, AI enabled product discovery techniques to synthesize customer feedback, operational telemetry, and usage data at scale.
Drive experimentation, A/B testing, and outcome based measurement using AI augmented analytics and observability tooling.
Ensure that AI and agentic capabilities are designed with strong governance, explainability, security, and compliance aligned to enterprise standards.
Collaborate with Enterprise Architecture and Platform teams to ensure AI capabilities are reusable, scalable, and aligned with long term data platform strategy.
Balance rapid AI innovation with enterprise grade reliability, risk management, and operational excellence.

Requirements

Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Economics, Business Analytics).
3+ years of experience in data product management, data strategy or analytics
Strong understanding of data platforms (Snowflake, Databricks etc.) and enterprise analytics ecosystem
Experience with the entire data pipeline, including data acquisition, data preparation, and database architecture.
Experience with data engineering projects supporting data science and AI or ML implementations.
Experience with Python and SQL.
Ability to support project teams in scaling, monitoring, and operating enterprise data platforms.
Well versed in using collaboration tools such as Jira and Confluence.
Experience working on real time data and streaming applications.
Experience with Agile engineering practices.
Ability to consult with customers to document technical requirements from business objectives and translate those requirements into system and platform architecture.
Experience creating solutions within a collaborative, cross functional team environment.
Excellent written and verbal communication skills, with the ability to communicate effectively across technical and non technical audiences.

Benefits

Healthcare (medical, dental, vision)
Basic term and optional term life insurance
Short-term and long-term disability
Pregnancy disability and parental leave
401(k) and employer-funded retirement plan
Paid vacation (from two to five weeks depending on salary grade and tenure)
Up to 11 paid holiday opportunities
Adoption assistance
Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data solutionsdata architecturesdata pipelinesdata warehousesdata analysisPythonSQLAImachine learningdata engineering

Soft Skills

problem solvingcollaborationcommunicationconsultative approachcustomer understandingdocumentationanalytical thinkingadaptabilityteamworkstakeholder engagement

Certifications

Bachelor's degreeMaster's degree