Analytics Engineer

• Design and build our data models and transformations, applying software engineering best practices in dbt on top of our Google BigQuery warehouse to produce datasets that are accurate, performant, and easy to reason about.
• Own the semantic layer in Lightdash, defining metrics, dimensions, and governed datasets so the business gets one consistent, trusted answer to every question — no matter who (or what) is asking.
• Act as a leader for our data modeling standards, shaping conventions, review practices, and documentation that keep the transformation layer maintainable as it grows.
• Develop end-to-end with AI coding agents. Our teams build with Claude Code and similar tools, and you'll use AI-assisted workflows to move faster while upholding quality through testing and review.
• Enable AI-driven analytics, treating the semantic and governance layers as the foundation that agents and MCP connections build on to deliver trustworthy, self-serve answers from data.
• Advance metric governance and definitional consistency, contributing to the auditability and reporting rigor expected of a company operating at scale and under growing scrutiny.
• Partner with cross-functional stakeholders to gather requirements, identify high-value reporting and dashboarding use cases, and ship the models and metrics that drive decisions.
• Train and enable users on self-serve analytics capabilities and best practices, growing the number of people across the business who can confidently answer their own questions with data.

Senior Analytics Engineer

• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.
• Ensure semantic objects conform to approved business taxonomy, metric definitions, and naming conventions.
• Gather and clarify requirements from commercial business users to ensure dashboard and reporting solutions meet their analytical needs.
• Provide first-line support for BI platform questions, report issues, and data interpretation queries from business users.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to the appropriate pipeline owners.
• Support the development and testing of agentic AI workflows that leverage governed semantic layer assets and BI data products for automated commercial insights delivery.

Commercial Analytics Engineer – Business Decision Intelligence

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs
• Update and maintain analytics & reporting solutions according to industry standard methodologies
• Find opportunities to consolidate analytics & reporting solutions
• Establish and monitor adoption criteria based on utilization statistics
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

Senior Engineer, Analytics & Productivity Solutions

• Design and maintain production-grade data pipelines and curated datasets that directly support pharmacovigilance activities, including safety monitoring, analytics, and regulatory reporting.
• Ensure data engineering solutions produce reproducible, explainable, and trusted analytics outputs suitable for safety decision support and inspection readiness.
• Enable AI/ML and GenAI workflows for safety analytics, including: Feature engineering and feature store enablement, Embeddings, vectorized representations, and semantic retrieval, Retrieval-Augmented Generation (RAG) patterns for safety analytics tools.
• Own the end-to-end data lifecycle for safety analytics, from source system intake through transformation, serving, and downstream analytical consumption, ensuring data continuity, traceability, and integrity.
• Lead architectural decisions across ingestion, transformation, storage, and serving layers on GCP (e.g., BigQuery, Dataform, object storage).
• Design, implement, and automate scalable, reusable data pipelines and architectures to support evolving safety analytics needs.
• Establish and enforce data quality, validation, lineage, and observability standards for safety analytics datasets.
• Define and implement data governance practices, including data contracts, schema versioning, access control, stewardship, and lifecycle management.
• Ensure safety analytics data and systems meet Global Medical Safety requirements for reliability, auditability, and regulatory use.
• Apply GxP validation expertise to data pipelines, analytics services, and supporting infrastructure.
• Partner with quality and compliance teams to implement CSV/CSA-aligned controls, audit trails, documentation, and organizational change.
• Design and build APIs and microservices-based architectures to operationalize safety analytics and ML capabilities (e.g., feature serving, retrieval services, analytics backends).
• Deploy and operate services on GCP (e.g., Cloud Run, GKE) with a strong focus on security, scalability, and observability.
• Serve as a technical authority and data engineering leader for Safety Analytics within Global Medical Safety.
• Review and influence designs across pipelines, services, feature stores, and AI/ML integrations to maintain a high technical bar.
• Collaborate closely with safety scientists, epidemiologists, biostatisticians, analytics teams, IT, and platform partners to translate safety needs into scalable technical solutions.
• Communicate complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders.

Principal Data Engineer – Safety Analytics

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption.
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs.
• Update and maintain analytics & reporting solutions according to industry standard methodologies.
• Find opportunities to consolidate analytics & reporting solutions.
• Establish and monitor adoption criteria based on utilization statistics.
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates.
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions.
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost.
• Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams.
• Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement.
• Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics.
• Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations.
• Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency.
• Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture.
• Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure.
• Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

Staff Software Engineer, Backend – Lake Analytics Platform

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Build and operate core platform capabilities: Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling.
• Develop integrations: Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need.
• Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks.
• Automate toil and unlock self-service: Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work.
• Improve observability and incident response: Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time.
• Support platform operations: Participate in on-call coverage, triage production incidents across platform services and the applications that run on them, and execute platform-level maintenance with care for the engineers and workflows that depend on the platform.
• Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities — semantic layer infrastructure, agentic application patterns, and AI-native data tooling — as the platform grows to support Affirm's most ambitious data use cases.
• Raise the engineering bar: Set and hold high standards in code review, design, operational readiness, and incident follow-through. Help establish patterns that other contributors can follow and build on.

Senior Software Engineer, Backend – Lake Analytics Platform

• Design, develop, and maintain high-quality Power BI dashboards, KPI scorecards, and data models.
• Translate business requirements into well-structured analytical solutions across manufacturing, supply chain, quality, finance, and commercial areas.
• Ensure consistency and global alignment on metrics, definitions, and UI/UX standards.
• Optimize Power BI performance, including DAX calculations, data modeling, and incremental refresh.
• Partner with business process owners to enable self-service analytics.
• Extract, model, and transform data from multiple global systems.
• Support data pipeline development for analytics and reporting.
• Ensure data quality and reliability across reporting ecosystems.
• Help identify and scope early AI use cases.
• Configure and support AI toolsets such as copilots and embedded SaaS AI.
• Ensure safe and compliant AI usage in partnership with IT Security.
• Provide training and documentation for AI-enabled tools.

Data Analytics & AI Solutions Engineer

• Lead, manage, and develop a high-performing team of Analytics Analysts
• Define and own the enterprise semantic layer strategy for commercial sales analytics
• Own the global self-service analytics operating model
• Lead the evaluation, selection, and adoption of semantic layer, BI, and analytics platform capabilities
• Define and drive the enterprise strategy for agentic AI integration within the commercial analytics platform
• Own and drive the strategic transformation of commercial analytics from reactive dashboard reporting to proactive, AI-ready data products
• Build and maintain trusted relationships with senior commercial leaders across Regions and Business Units

Associate Director, Global Sales Analytics Engineering – Business Decision Intelligence

• The Analytics Analyst designs, builds, and maintains dashboards, reports, and semantic layer assets that enable commercial business users to access governed, accurate insights.
• Working within defined data products and under guidance from senior analysts and leads, this role translates business requirements into self-service BI solutions and maintains the quality and usability of the analytics user interface.
• Dashboard & Report Development: Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Semantic Layer Development & Maintenance: Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.

Commercial Analytics Engineer – Go To Market Analytics

• Lead the design and implementation of governed semantic layer models aligned to enterprise business taxonomy, metric definitions, and data governance standards
• Define and enforce semantic layer design patterns, naming conventions, and object hierarchies that enable consistent metric consumption across all BI and analytics tools
• Partner with Commercial Data Product Strategy and Analytics Engineering teams to ensure physical data models are correctly surfaced through the semantic layer
• Maintain semantic layer health, resolving conflicts, deprecating legacy objects, and managing version-controlled releases
• Design and deliver advanced, scalable BI dashboards and user interfaces that serve as authoritative commercial reporting surfaces for senior business stakeholders
• Drive the reduction of bespoke report requests by designing modular, reusable analytics assets that empower business users to self-serve
• Act as a primary analytics liaison between commercial business teams, Data Product Strategy, and Analytics Engineering to align reporting needs with data model capabilities
• Lead the design and delivery of agentic AI workflows that consume semantic layer assets to automate commercial reporting, anomaly detection, and insight generation.

Senior Commercial Analytics Engineer – Business Decision Intelligence

• Define and drive the enterprise semantic layer strategy for commercial analytics
• Own the global self-service analytics strategy
• Define UX/UI standards and design principles for the commercial analytics interface
• Partner with Commercial Data Product Strategy to co-own the enterprise business glossary
• Define and drive the enterprise strategy for agentic AI integration within the commercial analytics platform
• Provide strategic technical leadership across the analytics team

Principal Analytics Engineer – Business Decision Intelligence

• The Analytics Analyst designs, builds, and maintains dashboards, reports, and semantic layer assets.
• Work within defined data products and under guidance to translate business requirements into self-service BI solutions.
• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform.
• Gather and clarify requirements from commercial business users to ensure analytic needs are met.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to pipeline owners.

• Support audit teams by fulfilling data requests from financial, accounting, and compliance systems across the company.
• Drive the strategic integration of AI tools to innovate and streamline the end-to-end audit process, from planning and risk assessment to execution and reporting.
• Develop analytics solutions and dashboards using datasets to provide real-time risk assessments and control insights through Power BI and other Microsoft tools.
• Contribute to team projects and deliver outputs using agile project management methods.
• Build automations that improve efficiency and save time using tools such as UiPath, Dataiku, and Python/R.
• Document technical solutions in line with SDLC requirements and guidance.
• Collaborate with and present to finance and analytics groups to share insights and leverage existing solutions.
• Explore and test new systems and technologies to expand team capabilities and generate new insights.
• Support the execution of financial, operational, SOX 404, IT, integrated, and process audits in domestic and international settings to help maintain a strong internal control environment.

Finance Data Analytics Specialist

• Design and implement a formal business data layer in dbt, ensuring that data from multiple sources is centralized, reconciled, and serves as a single source of truth for GTM metrics.
• Collaborate with stakeholders within the GTM Operations team and Finance to define, document, and maintain alignment on key business metrics and data definitions.
• Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards.
• Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review.
• Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences.

Analytics Engineer jobs in New Jersey

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Senior Analytics Engineer

Commercial Analytics Engineer – Business Decision Intelligence

Senior Engineer, Analytics & Productivity Solutions

Principal Data Engineer – Safety Analytics

Senior Engineer, Analytics & Productivity Solutions

Staff Software Engineer, Backend – Lake Analytics Platform

Analytics Engineer

Senior Software Engineer, Backend – Lake Analytics Platform

Data Analytics & AI Solutions Engineer

Associate Director, Global Sales Analytics Engineering – Business Decision Intelligence

Commercial Analytics Engineer – Go To Market Analytics

Senior Commercial Analytics Engineer – Business Decision Intelligence

Principal Analytics Engineer – Business Decision Intelligence

Commercial Analytics Engineer – Business Decision Intelligence

Finance Data Analytics Specialist

Staff Analytics Engineer

Senior SAS ETL Developer