Analytics Engineer

• Define, prototype, and productionize safety and performance metrics for automated driving systems.
• Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness.
• Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions.
• Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior.
• Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making.

Analytics Engineer, AV Safety Engineering

• Architect and own CodeRabbit's BigQuery warehouse as the canonical analytical layer, building on the existing GCP and Fivetran foundation and taking it to a governed, investor-grade standard.
• Design and ship the dbt project end to end, from raw sources through staging and intermediate models to consumer-facing marts, following modern layering and version-control best practices.
• Set the canonical definitions of ARR, NRR, bookings, and lifecycle in the dbt Semantic Layer so BI, Salesforce, and AI agents all read the same number.
• Build the canonical revenue models behind CodeRabbit's full billing model: seat subscriptions, usage-based add-ons, and per-marketplace settlement across our marketplace channels (AWS, GCP, Vercel, and others).
• Build the identity-resolution spine that resolves a single account and person across product, marketing, billing, and CRM, anchored on stable, system-generated identifiers.
• Partner with Growth Engineering to ship the GTM intelligence layer: PQL and PQA scoring, expansion signals, and the single sales-ready queue that reaches reps through reverse ETL and tools like Clay.
• Make the warehouse a first-class interface for AI agents, exposing the semantic layer and Agents Schema as the governed source agents query, with PQA scoring trained in BigQuery ML and Vertex AI.
• Own data governance, including PII protection and a consent and suppression model that gates downstream activation.
• Establish the data practices, definitions, and documentation the company runs on, and serve as the trusted technical partner to Finance, RevOps, Marketing, and Product.

Staff Analytics Engineer

• Design, develop, and maintain Python-based analytical applications supporting reporting, planning, and decision-making
• Build and support data pipelines and workflows, including ingestion, transformation, validation, and integration
• Develop and optimize SQL queries, database schemas, and data access patterns for performance and scalability
• Create user-facing dashboards, visualizations, and analytical interfaces to enable data exploration and insight generation
• Translate complex analytical requirements into scalable, maintainable software solutions
• Implement best practices in software development, including version control, testing, and code reviews
• Support application lifecycle management, including enhancements, troubleshooting, and performance optimization
• Collaborate with technical teams and stakeholders to define requirements and deliver high-quality solutions
• Ensure secure development practices and proper handling of data and application dependencies
• Document application architecture, workflows, and operational processes

Software Engineer, Analytics

• Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide analytics and decision-making.
• Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes key business metrics, dimensions, and data products, ensuring self-serve capabilities for stakeholders across teams.
• Partner cross-functionally with Product, GTM, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics.
• Establish and champion data modeling standards and best practices, guiding the organization in how to model data for accuracy, performance, usability, and long-term maintainability.
• Partner with Product Managers, Engineers, and Data teams to design tracking plans for new product surfaces, ensuring events are implemented accurately, consistently, and with downstream analytics use cases in mind.
• Own the product event tracking strategy, including event naming conventions, property schemas, identity resolution, sessionization, versioning, deprecation, and documentation standards.
• Empower stakeholders with data by making analytical assets easily discoverable, reliable, and well-documented – turning complex datasets into actionable insights for the business.
• You’ll define the structure, taxonomy, governance, and modeling patterns for product event data, ensuring that user behavior, product usage, and customer journeys are captured consistently from instrumentation through analytics-ready models.

Senior Analytics Engineer, Product

• Own and develop core data models, datasets, and dashboards that serve as the foundation for GTM reporting and decision-making
• Enable a single source of truth for GTM metrics by partnering with cross-functional stakeholders to define, standardize, and operationalize key business logic
• Deliver insights and recommendations that improve GTM performance and inform strategic decisions
• Build scalable data systems and workflows that improve data accessibility, reliability, and usability across the organization
• Develop AI-ready data models and self-service solutions that enable natural language access and automated insights
• Partner closely with Sales, Revenue Operations, Finance, and Marketing to align on priorities and drive impact

Senior Analytics Engineer, GTM

• Understand the data needs of stakeholder teams in terms of key data models and reporting, and translate them into technical requirements
• Develop deep expertise in the ClickHouse product and platform, becoming the connective tissue between product surfaces, engineering telemetry, and stakeholder reporting
• Own and develop core data models, datasets, and dashboards that serve as the foundation for product decision-making across growth and the broader ClickHouse product portfolio
• Partner closely with Product, Engineering, and Growth to align on metric definitions, instrument new features, and operationalize a single source of truth for product data
• Build scalable data systems and workflows that improve data accessibility, reliability, and usability across the organization
• Develop AI-ready data models and self-service solutions that enable natural language access and automated insights
• Deliver insights and recommendations that inform product strategy, roadmap prioritization, and growth experimentation.

• Develop and deploy Custody-focused reports and dashboards
• Validate, modify, and document Radar automatic and custom SQL metrics
• Write stored procedures for clinical reporting requests for use in Cogito SQL templates
• Create Radar components from Cogito SQL reports and metric frameworks
• Update code and dashboard metrics based on new regulatory and operational requests
• Lead projects and facilitate meetings with operational leadership to gather requirements for new requests and enhancements
• Estimate timelines needed to complete projects and communicate updates to stakeholders
• Provide status updates, manage expectations, and communicate risks/issues to stakeholders inside and outside of analytics
• Direct regulatory agency monitors to reporting outputs and provide guidance on how to use them
• Follow SDLC standards for unit testing, peer reviews, SME validation, production migration, and publishing
• Support knowledge transfer, mentorship, SQL code reviews, and peer review of deliverables

BI Reporting and Analytics Developer/Analyst – Custody

• Partner with product, business, and engineering teams to identify data needs, define metrics, and enable data-driven decision making
• Design, build, and own scalable data pipelines and data models that power analytics, reporting, and product insights
• Develop and maintain core datasets for financial and usage behavioral analytics, enabling deep understanding of customer behavior, product adoption, and business performance
• Help define, instrument, and validate product and business metrics, including those exposed in customer-facing dashboards
• Drive improvements in data quality, reliability, and observability, including ownership of SLAs for critical pipelines
• Collaborate with engineering teams to evolve data models alongside product changes
• Contribute to the definition of our analytics layer (e.g., dbt models, semantic layer, metric definitions)
• Create and maintain clear technical documentation and data contracts

Senior Analytics Engineer

• Design and develop scalable data models that power our data products
• Collaborate with key stakeholders across product, engineering, operations, and data science teams
• Contribute to and improve our dbt implementation and data models
• Collaborate with partners across technical and non-technical teams
• Support key decision makers about the state of the business
• Participate in development, testing, documentation, and evangelism of core data models
• Utilize analytical tools like Databricks, BigQuery, Airflow, Mode, and Tableau

• Build and maintain partner data pipelines across APIs, S3, and event flows
• Develop Python and SQL scripts for ingestion, transformation, and delivery
• Own partner integrations including onboarding, monitoring, and issue resolution
• Build dashboards for subscriber metrics, revenue, and partner performance
• Perform analysis on funnel, retention, billing, and attribution
• Troubleshoot data discrepancies and manage incident response
• Define KPIs, data models, and integration specifications
• Support QA and UAT for integration releases
• Work with Product, Engineering, Finance, and external partners

Data Analytics Engineer – Subscription & Partner Integrations

• Build and improve data models that enable faster, more reliable decision-making across Product, Risk, and Analytics
• Reduce bottlenecks by delivering clean, scalable datasets analysts can trust and use independently
• Support both roadmap initiatives and ad hoc needs to keep business teams moving without delays
• Improve data quality and structure so new products and features can be analyzed and launched with confidence

Analytics Engineer II

• Build and own dbt models for the financial subledger platform (staging → intermediate → canonical facts/balances/marts → semantic), including naming conventions, macros, and reusable patterns.
• Implement strong data quality and controls in dbt: tests (unit/relationship/assertions), freshness, anomaly checks, and automated reconciliations that support close and audit readiness.
• Embed AI-assisted reconciliation capabilities into the platform (within appropriate security/controls guardrails): automate variance triage, suggest likely root causes, and generate human-reviewable reconciliation narratives and evidence artifacts.
• Own end-to-end subledger data products (e.g., event/fact layers, balance rollforwards, reconciliation outputs) with traceability from source events through transformations to reporting outputs.
• Partner with Accounting/Financial Reporting to translate requirements into clear model specifications (definitions, posting logic assumptions, tie-out rules) and ship them as durable dbt assets.
• Drive production operational ownership: monitoring/alerting, incident response, root-cause fixes, and release hygiene for the pipelines and models you own.
• Collaborate with upstream engineering teams to define inputs and improve source data quality via contracts and change management.
• Coach and develop one Analytics Engineer (Poland) via code review, pairing, scoped ownership, and clear technical direction.

Staff Analytics Engineer, Subledger Platform

• Maintain and expand the company’s analytical data model using Snowflake and dbt, ensuring datasets are reliable, well-structured, and easy to use.
• Partner with stakeholders to understand reporting and analytics needs and translate them into new models and datasets.
• Investigate discrepancies in metrics and datasets and perform root-cause analysis across systems.
• Monitor and maintain ELT pipelines across our data stack.
• Investigate and resolve pipeline failures, schema changes, and data inconsistencies.
• Identify opportunities to improve pipeline reliability, efficiency, and cost effectiveness.
• Expand documentation across the data model to clearly describe business logic, relationships, and definitions.
• Ensure datasets are clearly structured and documented so they can be reliably used across analytics tools and internal workflows.
• Contribute to the structured AI data context files that help internal AI tools accurately interpret datasets and metrics.
• Help maintain data governance standards, including contributing to PII masking policies and ensuring sensitive customer data is handled appropriately across the data platform.
• Work closely with Product, Revenue, and Operations teams to understand their data needs and questions.
• Help stakeholders navigate the data model and identify the most appropriate datasets for their use cases.
• Occasionally build or modify Hex projects to support data exploration or reporting needs.

• Design, implement, and support end‑to‑end ELT pipelines (ingest → transform → publish) in Databricks/ADF
• Implement data quality checks (DLT expectations, unit tests) with alerting and remediation runbooks
• Build curated, analytics‑ready Delta tables using dimensional modeling for consumption by BI Developers
• Implement CDC and deletion‑flag patterns; manage schema drift and partitioning/Z‑Ordering strategies
• Operationalize jobs with monitoring, logging, alerting; participate in an on‑call rotation as needed
• Partner with the Data Architect to align designs with standards for governance, security, and cost efficiency
• Document pipelines, data contracts, and SLAs; continuously improve performance and reliability

Data Solutions Engineer

• As an Analytics Engineer you will derive impactful insights from data to drive intelligence for Unified and its clients.
• You will work with your fellow team members to define and scope solutions to solve complex problems.
• You will help build, improve, and test robust reporting and statistics solutions that will drive impact throughout the organization.

See all jobs on JobTailor

Analytics Engineer, AV Safety Engineering

Staff Analytics Engineer

Software Engineer, Analytics

Senior Analytics Engineer, Product

Senior Analytics Engineer, GTM

Senior Analytics Engineer, Product

BI Reporting and Analytics Developer/Analyst – Custody

Senior Analytics Engineer

Analytics Engineer

Data Analytics Engineer – Subscription & Partner Integrations

Analytics Engineer II

Staff Analytics Engineer, Subledger Platform

Analytics Engineer

Data Solutions Engineer

Analytics Engineer