Data Analyst

Job Description & Responsibilities

Persistent Systems is seeking a Data Analyst to join our team in our New York City headquarters. The Data Analyst will transform complex operational, financial, customer, product, and program data into accurate, timely, and actionable insights. This role will build trusted datasets, semantic models, dashboards, and analyses that help leaders understand performance, identify opportunities, manage risk, and make evidence-based decisions.

The ideal candidate is a disciplined problem solver and an effective "data translator" who can move comfortably between business questions and technical details. They combine strong SQL, data preparation, visualization, statistical reasoning, and stakeholder communication skills with a sharp eye for data quality and user experience.

• Own analytical work from requirements discovery and source-data assessment through transformation, modeling, validation, visualization, delivery, and ongoing support
• Partner with leaders and subject-matter experts across Finance, Operations, Marketing, Sales, Product, and other functions to define key performance indicators (KPIs), reporting logic, and success measures
• Develop reliable, reusable analytics assets and clear documentation so that metrics, assumptions, transformations, and data lineage can be understood and maintained
• Apply statistical and analytical methods appropriate to the business question, communicate uncertainty and limitations, and distinguish correlation from evidence of causation
• Promote secure, governed, accessible, and responsible use of data while improving self-service analytics across the organization
• Design, build, and maintain intuitive dashboards, scorecards, semantic models, and recurring reports using Power BI as the primary platform and Tableau where appropriate
• Write, review, and optimize complex SQL queries across relational databases, cloud warehouses, and Lakehouse platforms; reconcile results to source systems and explain query logic
• Clean, profile, map, join, and transform structured and semi-structured data using Power Query, Python/pandas, spreadsheets, and related tools; design fact and dimension models using star-schema principles
• Build or contribute to repeatable extraction, transformation, and loading workflows using frameworks and platforms such as dbt, Azure Data Factory, Fabric Data Factory, AWS Glue, and Databricks; monitor failures and document lineage
• Analyze historical and current data to identify trends, drivers, anomalies, risks, and opportunities; perform segmentation, variance analysis, forecasting, cohort analysis, and experiment analysis when relevant
• Create validation rules, reconciliations, exception reporting, and quality checks; investigate discrepancies, perform root-cause analysis, and coordinate durable fixes with data owners and engineering teams
• Translate open-ended questions into measurable definitions, source-to-target mappings, acceptance criteria, and analytics plans; establish consistent KPI definitions and a shared business glossary
• Reduce manual reporting through scheduled refreshes, parameterized workflows, Power Automate, reusable templates, and governed self-service datasets
• Present concise findings and recommendations to technical and nontechnical audiences, using accessible visual design and clear narrative rather than relying on charts alone
• Support embedded or custom analytics experiences using JavaScript visualization libraries and component-based charting tools such as Recharts, D3.js, Plotly.js, Chart.js, and shadcn/ui chart components
• Explore responsible uses of Copilot Studio, Microsoft Foundry, and generative AI frameworks to accelerate discovery, natural-language analytics, documentation, and…