Top Data Analyst Jobs in NYC, NY

Own client data onboarding by mapping source systems into a software platform, loading and validating messy files, troubleshooting live data issues, and documenting repeatable procedures. Explain technical findings to non-engineering teams, trace discrepancies to root causes, and collaborate with technical leaders on reusable checks, automation, and governance standards. Balance onboarding responsibilities with ongoing data troubleshooting.

Analyze, clean, transform, and integrate data from multiple sources; identify trends, anomalies, KPIs, and business insights; and create scalable Tableau dashboards, reports, and visualizations. Develop Tableau Prep workflows, support data governance, optimize data retrieval with engineering and IT teams, and communicate actionable recommendations to technical and executive stakeholders. The role also uses statistical analysis, ServiceNow operational analytics, and business intelligence tools.

Leads business analytics engagements, translating stakeholder needs into data requirements, models, dashboards, forecasts, statistical analyses, and actionable recommendations. Performs data exploration, querying, modeling, and visualization across large datasets; supports data governance, quality, security, and stewardship. Mentors analysts, communicates insights to leaders, collaborates with enterprise data teams, and identifies opportunities to apply machine learning in business decision-making.

Leads complex analytics initiatives for the Consumer Deposits portfolio, owning Net Present Value methodology and models. Translates ambiguous business questions into scalable datasets, analytical products, and actionable insights supporting pricing, marketing, product, customer, and strategic decisions. Partners with senior stakeholders and cross-functional teams, manages projects independently, improves analytical methodologies and governance, and mentors analysts.

Analyzes and interprets data, evaluates results, and provides actionable insights to support strategic decision-making across departments.

Analyze banking and financial crime data, develop and tune statistical and machine learning classification models, evaluate model performance, identify data quality issues, and recommend optimization strategies. The role uses Python and SQL for feature engineering, statistical modeling, automation, and large-scale data processing. Responsibilities also include documenting methodologies, reporting results to management and regulators, and collaborating with compliance, technology, business, and model governance teams.

Lead product analytics for Enterprise Data products, validating analytical tools, assessing methodology risks, researching emerging practices, and translating complex data into actionable insights. Manage validation projects, communicate findings through reports and presentations, and coach junior staff. The role requires advanced data analysis, statistical or econometric modeling, predictive analytics, large-dataset analysis, data governance, and people leadership.

Analyze multi-platform, multi-market media campaign data, connect ad delivery metrics with campaign KPIs, validate and transform datasets, run AWS and BigQuery queries, create visualizations, and provide insights supporting sales and client account performance.

Analyze marketplace and product performance using SQL, support experiments and advertising-effectiveness studies, build reusable datasets, QA metrics, and document analytics. Collaborate with analytics engineers, data scientists, product managers, engineers, and commercial teams while developing skills in statistics, causal inference, Python/R, and marketing science.

Conduct end-to-end data analysis, translate ambiguous business questions into actionable recommendations, and support decision-making across company functions. Build and maintain dbt models, tests, documentation, curated tables, semantic-layer metric definitions, and BI reporting. Partner with data engineering and stakeholders to ensure data quality, governance, and self-service access for people and AI agents. Maintain dashboards and identify opportunities across multiple business verticals.

Analyze mobile app activation, feature adoption, onboarding, engagement, retention, funnels, cohorts, and A/B tests. Build SQL and dbt models, dashboards, and self-service reports using BigQuery and GCP. Partner with product and engineering teams on instrumentation, identify data issues, automate reporting with AI tools, and communicate actionable insights to nontechnical stakeholders.

Own analytics engineering and the data transformation layer by designing data models, warehouses, cubes, semantic layers, and governance frameworks. Improve data quality, observability, metadata, self-service analytics, dashboards, and reporting platforms. Analyze large datasets, optimize queries, conduct testing and code reviews, support modernization initiatives, and collaborate across teams to deliver trusted metrics and insights.

Owns end-to-end customer analytics workstreams, including KPI frameworks, multi-source dashboards, reporting, data governance, quality assurance, and insight delivery for government digital communications programs. Presents recommendations to stakeholders, mentors junior analysts, leads Agile ceremonies, documents analytics practices, resolves data issues, and evaluates AI-assisted analytics tools.

Oversee compliance, data quality, documentation, referral closure, eligibility timeliness, billing readiness, and audit preparedness for Social Care Network activities. The analyst audits records, tracks performance metrics, identifies risks, maintains policies and procedures, supports corrective actions, and leads internal and external audit coordination. The role collaborates with navigators and leadership to ensure alignment with New York State 1115 Waiver, PHS, SOMOS, and program requirements.

The Finance Data Analyst manages finance ETL processes, reconciles enterprise data warehouse outputs, investigates discrepancies, and validates insurance financial data. Responsibilities include SQL-based analysis of premiums, commissions, taxes, fees, and payments; building automated controls and exception reports; supporting Finance reporting and close activities; documenting data processes; and partnering with Finance and data engineering teams to improve data quality and reliability.

Develop and optimize advanced SQL transformations, stored procedures, ETL workflows, and data-quality processes for large Medicaid claims and health-related social needs datasets. Build reproducible, documented data pipelines; calculate healthcare quality, equity, and disparity metrics; conduct code reviews; and collaborate with stakeholders to turn complex data into visualizations and policy insights.

Develops dashboards, data models, reports, and analytical solutions that transform raw data into actionable business insights. Partners with stakeholders, provides technical guidance, documents architectures and processes, troubleshoots analytics solutions, and communicates findings to technical and non-technical audiences. Requires advanced Spotfire, SQL, business intelligence, and data analytics expertise, with knowledge of Databricks, Power BI, Python, R, Salesforce, and modern data architecture.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Implements and supports software solutions, configuring platforms to client-specific data and business needs. Mines, cleans, maps, validates, and analyzes large datasets; communicates actionable insights; calibrates models; evaluates model performance statistically; and improves configuration support processes. The role also serves as a client-data subject matter expert, advises on data initiatives, and supports multiple concurrent projects.

Manage client-facing reports, data quality, reporting requests, and forecast models. Analyze business requirements and data to develop technical specifications, verify report accuracy, identify bottlenecks, and support immigration case teams, attorneys, paralegals, and clients. Maintain data quality scorecards, process guides, and reporting workflows while ensuring timely delivery and adherence to client standards and SLAs.

Perform forensic data analysis and fraud detection using SQL, Python/R, ML, big-data tools and visualization. Develop detection models/rules, evaluate anti-fraud tools, manage engagements, communicate findings to stakeholders, and lead project teams in investigations and compliance remediation.

Analyze product and user behavior data to guide roadmap decisions, define success metrics, lead A/B experiments, maintain event tracking, monitor product health, build reusable datasets and reports, and present recommendations to cross-functional stakeholders. The role partners closely with product, design, and engineering teams in a fast-paced sports prediction market.

Maintains data accuracy, completeness, transparency, and integrity within case tracking systems. Monitors inbound data feeds, analyzes data quality issues, resolves escalated system problems, supports integrations, and identifies enhancement opportunities. Develops reports, performance metrics, reproducible analyses, templates, and content for internal portals. Serves as a subject matter expert, mentors Tier 1 staff, researches technology solutions, and supports analysis and reporting across agency bureaus.

Paid data analyst intern to collect, clean, and analyze e-commerce and marketing datasets, build dashboards, generate customer and performance insights, support market research, and collaborate with marketing, product, and sales teams to drive data-informed decisions.

Build and maintain Tableau dashboards, Snowflake Streamlit applications, and dbt models for the Developer Led Growth team. Analyze experiments and initiatives, create measurement frameworks, identify data gaps and opportunities, and translate findings into actionable recommendations. Own analytics prioritization, communicate with technical and executive stakeholders, and use SQL, AI tools, and automation to scale analytical processes.