Analytics Engineer

• Work to make Wave’s data easy, safe, and reliable to use, enabling better products for our customers.
• Leading dimensional design to structure Wave’s analytics data.
• Managing our extensive snowflake estate with a strong analytical mindset.
• Building tools to improve the analytics development experience.
• Responsible for the design, build and health of all of our ETL pipelines.

Senior Analytics Engineer

• 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

• Developing novel, parallel algorithms to accelerate core problems in data processing and power the next generation of data center workflows
• Implementing solutions in C++ and CUDA
• Contributing to open source projects, such as cuDF, Velox, Presto and Spark
• Benchmarking, profiling, and optimizing code
• Working closely with the world’s top experts in GPU hardware, software and workflows

Senior Software Engineer, C++, CUDA – Analytics, Data Intelligence

• Partner with our Commercial team to understand customer usage of Antenna data
• Manage the end-to-end data product release
• Develop and deploy strategic product improvements
• Build and optimize systems to monitor data quality
• Act as the first responder for data issues and inquiries
• Manage updates to QA tests and dashboards
• Investigate data restatements and coordinate data fixes
• Perform data quality investigations to align Antenna data with customer data
• Run new coverage feasibility for updates in methodology and pipeline
• Coordinate monthly and weekly data releases

Data Analytics Engineer

• Convey the strategic advantages and technical application of Snowflake's Analytics capabilities, address client inquiries, and conduct follow-up engagements to facilitate sales progression.
• Articulate Snowflake's Analytics functionalities and provide a comparative analysis with competitor offerings for prospective clients.
• Ascertain client-defined success metrics for the adoption and utilization of Snowflake's Analytics features.
• Recognize, address, and reconcile discrepancies between Snowflake's methodologies and client-specific requisites concerning Analytics features.
• Provide comprehensive resolutions to client objections and concerns.
• Develop and deliver bespoke demonstrations that highlight Snowflake's value proposition, tailored to address specific client requirements pertaining to analytic functionalities.
• Demonstrate the ability to elucidate Snowflake's security, privacy, and governance considerations with respect to Analytics.
• Be the technical expert in the room that positions Snowflake’s analytical features and value to technical stakeholders at Snowflake’s customers across the Americas.
• Function as the principal technical authority, articulating Snowflake's analytical functionalities and value proposition to technical stakeholders within customer organizations across the Americas.
• Collaborate with Snowflake account teams and customer advocates to define and execute Proof of Concepts, ensuring successful outcomes and demonstrable technical achievements that validate Snowflake's capabilities, including comprehensive executive summaries and business value assessments.
• Develop and disseminate content to facilitate team and organizational growth, such as blog articles, conference presentations, and technical materials including notebooks and demonstrations.
• Act as the Voice of Customer by triaging Product gaps and communicating their priority to Product Management.
• Develop sales programs with Product Marketing around GTM for Analytics features.

Senior Solutions Engineer, Applied Field Engineering, Analytics

• Design and implement scalable data models in BigQuery using dbt that power analytics, reporting, and operational insights
• Develop systems for delivering data to internal stakeholders and external clients
• Maintain data governance practices including documentation, lineage, testing, and data quality standards
• Optimize query performance and cost efficiency for analytics workloads in BigQuery
• Partner closely with product, sales, analytics, and executive stakeholders to translate business requirements into reliable data models
• Mentor engineers and analysts on analytics engineering and data modeling best practices
• Contribute to the broader data platform, collaborating with engineers building real-time data systems and infrastructure

Senior Data Engineer – Analytics Platform

• Assist in the design, development, and maintenance of analytical reporting ETL pipelines and related front-end interfaces (e.g., dashboards), leveraging data languages such as SQL and Python.
• Contribute to the development and execution of QA and UAT protocols, proactively identifying and resolving data quality issues.
• Support training and ongoing support to data users, enabling them to effectively utilize data solutions and tools.
• Collaborate on the development of advanced analytics prototypes to drive strategic initiatives and business outcomes.
• Maintain comprehensive documentation to support scalability and knowledge transfer across the organization.

• Serve as a senior technical and analytical resource for Brown Advisory’s Addepar data ecosystem.
• Support, enhance, and scale Addepar workflows across portfolio management, client reporting, investment operations, performance analytics, and advisory processes.
• Analyze, troubleshoot, and resolve complex data issues involving accounts, entities, ownership structures, portfolios, holdings, transactions, securities, classifications, attributes, benchmarks, performance, and reporting outputs.
• Build, maintain, and improve data pipelines between Addepar and enterprise platforms, including Snowflake, reporting tools, client platforms, portfolio systems, accounting systems, Canoe, and other vendor applications.
• Use Addepar APIs, extracts, imports, and platform tools to enable data integration, automation, enrichment, validation, and downstream consumption.
• Develop SQL queries, reconciliation logic, exception reports, data profiling routines, and automated data quality controls.
• Partner with business and technology stakeholders to translate portfolio management, client reporting, operations, and analytics needs into data requirements, technical specifications, and scalable delivery solutions.
• Support ingestion, transformation, normalization, and validation of portfolio, client, account, security, transaction, performance, benchmark, alternative investment, and reference data.
• Contribute to Snowflake-centered data architecture by supporting secure data ingestion, modeling, quality checks, consumption-layer design, and integration with analytics and reporting platforms.
• Collaborate with investment operations and reporting teams to validate performance, exposure, allocation, classification, and client reporting data.
• Support production data operations, including data-feed monitoring, exception management, issue triage, root-cause analysis, release validation, vendor escalation, and post-incident remediation.
• Identify opportunities to automate manual workflows, reduce recurring data exceptions, improve data quality, and strengthen operational controls.
• Maintain clear documentation for data flows, mappings, business rules, reconciliation procedures, platform processes, and production support runbooks.
• Participate in platform enhancements, regression testing, user acceptance testing, release planning, and change-management activities.
• Ensure Addepar-related data processes align with Brown Advisory’s standards for security, privacy, auditability, operational resilience, and financial services regulatory expectations.
• Partner with architects, engineers, platform owners, business analysts, vendors, and data governance stakeholders to continuously improve the reliability, scalability, and usability of the Addepar data environment.

Senior Data Engineer, Analyst

• Partner with stakeholders to understand data needs and translate business questions into technical requirements for data models, pipelines, dashboards, and self-service tools.
• Design, build, document, and maintain reliable data pipelines using dbt, Dagster, Redshift, and related tools.
• Transform raw data into trusted, canonical datasets for reporting, analysis, experimentation, and decision-making.
• Build high-performance dashboards and analytical tools in Hex, Tableau, or similar platforms.
• Use AI tooling to accelerate pipeline development, SQL/dbt work, and documentation.
• Partner with data scientists, analysts, and engineers to bridge business needs and data infrastructure.

Analytics Data Engineer

• Architect, build and maintain efficient and reliable data products (dbt models, pipelines, Looker explores, visualizations, etc.) that enable data driven decision making.
• Build and maintain ELT pipelines from source systems to data warehouses.
• Influence strategy and drive execution of our analytical roadmap delivering self-service business intelligence at scale.
• Provide leadership and mentorship to other engineers fostering collaboration, knowledge sharing and a high bar for quality.
• Improve end users trust and confidence by driving best practices with regards to data discoverability, observability, lineage and stewardship.
• Contribute towards a robust data security strategy protecting sensitive information and content.
• Solve critical business problems through the application of programming, optimization, automation, and analytical skills.
• Drive functional strategy for AI adoption, identifying transformational opportunities that create competitive advantage in how we build and scale our data products.
• Build scalable AI-powered systems that fundamentally change how analytical work gets done across multiple teams.
• Serve as a trusted advisor to leadership on AI capabilities, limitations, and strategic implications for our data platform.
• Establish governance frameworks and best practices for responsible AI usage at scale within the analytics function.
• Transform core data and analytics processes through strategic AI integration—including intelligent pipeline monitoring, automated data quality checks, and AI-assisted insight generation.
• Quantify AI impact on key data metrics and make data-driven recommendations for continued investment.

• 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.

Staff Analytics Engineer

• Build and maintain scalable data models and pipelines using dbt and BigQuery.
• Develop measurement frameworks, dashboards, and self-service analytics solutions that drive product decisions.
• Partner with Product, Engineering, and Data Science teams to define metrics and improve how we understand product performance.
• Translate business questions into reliable, well-modeled datasets and actionable insights.
• Improve data quality, observability, documentation, and governance across the Music Mission.
• Contribute to responsible AI practices, ensuring data and insights are trustworthy, transparent, and scalable.
• Participate in a support rotation for critical datasets and analytics products.

Analytics Engineer – Music Creator Growth

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Analytics Engineer jobs in New York

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Senior Analytics Engineer

Staff Software Engineer, Backend – Lake Analytics Platform

Analytics Engineer

Senior Software Engineer, Backend – Lake Analytics Platform

Senior Software Engineer, C++, CUDA – Analytics, Data Intelligence

Data Analytics Engineer

Senior Solutions Engineer, Applied Field Engineering, Analytics

Senior Data Engineer – Analytics Platform

Analytics Engineer

Senior Data Engineer, Analyst

Analytics Data Engineer

Senior Analytics Engineer

Staff Analytics Engineer

Analytics Engineer – Music Creator Growth

Data Analytics Engineer