Data Analyst

Persistent Systems•New York, NY

10d•$133,000 - $166,000•Onsite

About The Position

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.

Requirements

2–5 years of professional experience in data analytics, business intelligence, reporting, or a closely related role
Bachelor’s degree in data science, statistics, mathematics, economics, finance, computer science, information systems, or a related quantitative field, or equivalent practical experience
Advanced proficiency in SQL, including joins, common table expressions, window functions, aggregation, query validation, and performance-aware analysis
Strong hands-on experience with Power BI, including Power Query, DAX, semantic modeling, interactive report design, refresh strategies, workspace management, and row-level security; comparable Tableau depth is also valued
Advanced spreadsheet skills and practical experience reconciling, cleaning, and validating data from multiple sources
Working knowledge of dimensional data modeling, KPI design, data dictionaries, ETL/ELT concepts, and source-to-target mapping
Experience using Python with pandas and/or R for data manipulation, exploratory analysis, or statistical work
Understanding of descriptive statistics, sampling, confidence intervals, hypothesis testing, and common analytical pitfalls
Demonstrated ability to gather requirements, manage multiple priorities, communicate findings clearly, and influence decisions with evidence
Must have a United States Security Clearance or be able to obtain a United States Security Clearance
Relevant certifications or credentials, such as: Microsoft Certified: Power BI Data Analyst Associate (PL-300), Microsoft Certified: Fabric Analytics Engineer Associate (DP-600), Tableau Certified Data Analyst, Tableau Desktop Specialist certification
Experience operating in a regulated, security-sensitive, government, or defense-adjacent environment

Nice To Haves

Experience with ETL/ELT frameworks and orchestration platforms such as dbt, Azure Data Factory, Fabric Data Factory, AWS Glue, Databricks, or comparable tools
Experience building solutions with Power Platform, Power Apps, and Power Automate, including governed connections to enterprise data
Experience with Microsoft Copilot Studio and Microsoft Foundry for secure AI-assisted analytics, agent experiences, or workflow augmentation
Familiarity with generative AI frameworks such as LangChain, LangGraph, or LangChain and the ability to identify appropriate, verifiable analytics use cases
Experience with JavaScript visualization libraries such as Recharts, D3.js, Plotly.js, or Chart.js, including shadcn/ui chart components built on Recharts
Experience with cloud data warehouses or Lakehouse platforms such as Microsoft Fabric, Databricks, Snowflake, Google BigQuery, or Amazon Redshift
Experience with Git, analytics testing, version-controlled SQL or semantic models, CI/CD, and collaborative development practices
Experience with data catalogs, lineage, access controls, sensitivity labels, or governance platforms such as Microsoft Purview
Experience designing experiments, performing A/B test analysis, building forecasts, or applying regression and other classical statistical methods

Responsibilities

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 workflow assistance while validating outputs against trusted data