Data Analyst Jobs in Jersey City, NJ

Build and maintain data pipelines from manufacturing and business systems into reporting datasets. Manage data lineage, quality, validation, reconciliation, governance, and SOX-compliant controls. Partner with IT, Finance, Operations, Supply Chain, and other teams to resolve data issues and define business metrics. Use Python, pandas, SQL, Git, and Jira to deliver reliable business intelligence solutions and support dashboards and reports.

Build and maintain data pipelines from ERP, manufacturing, supply chain, finance, and other systems into reporting datasets. Document data lineage, perform validation and reconciliation, maintain SOX-compliant processes, and resolve data quality issues. Collaborate with IT and business teams to define metrics and deliver reliable reporting solutions using Python, pandas, SQL, Git, and Jira.

Support data management and analytics for investment strategies by ensuring accuracy and integrity of portfolio data, performing reconciliations, automating end-to-end workflows, collaborating with technology and business stakeholders, and delivering recurring analytics and reports for sales, marketing, risk, and investment teams.

Summer Analyst in Data & AI will design and build end-to-end data, analytics, and ML/AI solutions using cloud and modern tooling. Responsibilities include developing models and pipelines, running experiments tied to business KPIs, supporting data governance and compliance, creating dashboards, and collaborating in agile teams while receiving mentorship and training.

Participate in a 24-month rotational data and analytics program covering business data analysis, business intelligence, data architecture, data engineering, and data governance. Gather and analyze data, create reporting and visualizations, support data pipelines and modeling, and help maintain data quality, security, compliance, and lifecycle standards. Collaborate with business partners and develop professional skills through training, mentoring, and on-the-job experiences.

Analyzes and corrects claims data errors, investigates root causes, evaluates business impact, and recommends long-term data quality improvements. The role manages claims corrections using business rules, resolves exceptions across systems, collaborates with claims administrators, programmers, vendors, and business teams, and produces ad hoc reports. Strong SQL skills and insurance claims knowledge are required; Snowflake experience is preferred.

Manage market data service delivery and client projects, handle vendor and entitlement administration, contract negotiation, procurement, invoicing and exchange reporting, oversee inventory and leavers/joiners, identify cost savings, and coordinate with internal stakeholders to meet SLAs.

Supports Verisk’s AI and data transformation roadmap for P&C insurance loss costs. The role designs actuarial workflows and data logic, tests AI-enabled tools and automation, validates data-driven solutions, and collaborates with operational and technical teams. The analyst owns defined project components while developing actuarial and technical expertise toward ACAS, with exposure to pricing, underwriting, customer-facing infrastructure, generative AI, and complex insurance datasets.

Support data center construction cost planning, forecasting, and controls across project lifecycles. Maintain program-level cost forecasts, prepare estimates and commercial risk assessments, compile benchmarking data, and coordinate with engineering and external cost consultants to optimize capital efficiency and inform location and deployment decisions.

Lead analytics projects and forecasting using Power BI, SQL, Python and DAX. Develop data models, resolve data quality issues, produce dashboards and management reporting, mentor junior analysts, collaborate with finance and stakeholders to deliver actionable insights, and drive continuous improvement and automation to support strategic decision-making.

Verify and maintain database master and control file metadata; identify, research, and resolve data errors; generate and respond to reports; document system files and reports; recommend improvements and coordinate modifications; learn legacy source systems and perform source-to-target data mapping to support analytic data lineage.

Leads business and data analysis for payment platforms including ACH, Wire, SWIFT, RTP/FedNow, and cross-border payments. Defines requirements, data mappings, architectures, testing strategies, and control frameworks while partnering with business, engineering, architecture, risk, and compliance teams. Uses Python, SQL, BI tools, and AI/ML platforms to generate insights, automate reporting, improve reconciliation, predict failures, and strengthen fraud and regulatory monitoring. Supports ISO 20022 modernization and intelligent, scalable payments operations.

Serve as product-owner/business analyst for actuarial data and tools within the Global Data Office. Translate actuarial requirements into data engineering specs, lead Agile ceremonies, manage product backlog and roadmaps, align stakeholders, and ensure data governance, lineage, testing, and quality for actuarial reporting, reserving, and pricing use cases.

Support privacy governance and regulatory compliance across the Americas by advising business units, managing privacy risk, conducting privacy impact assessments, maintaining records of processing activities, coordinating incident and forensic responses, developing policies and training, and working with Legal and Compliance to implement privacy controls for new technologies.

Senior Business Analyst in wealth management focused on data governance: define Key Data Elements, business rules, and data quality controls; profile and analyze data; validate lineage; translate business requirements into technical specs; lead UAT and testing; manage data quality monitoring and remediation; build cross-functional stakeholder relationships and advocate data-as-an-asset.

Serve as the actuarial-focused business analyst and product owner in the Global Data Office. Translate actuarial requirements into data engineering specs, own roadmaps for actuarial data and tools, manage product backlog and agile ceremonies, and ensure data governance, quality, lineage, and stakeholder alignment.