Analytics Engineer

• Build the core analytics data model – Design and maintain scalable fact and dimension models in our cloud data warehouse that support enterprise reporting and analysis.
• Create a trusted reporting layer across business systems – Integrate and model data from ERP, financial, project management, and operational platforms (e.g., NetSuite, Procore, Epicor, Snowflake, Timestream) to create a consistent source of truth.
• Preserve critical business context across systems – Identify where important financial or operational detail is lost in source systems or handoffs, and design data structures that retain the fidelity needed for analysis.
• Establish modeling standards and analytics best practices – Help define scalable approaches to naming, structure, metric logic, testing, and maintainability across the analytics environment.
• Deliver meaningful reporting and dashboards – Build and maintain dashboards and semantic models in our BI tooling that give stakeholders visibility into financial performance, project execution, and operational KPIs.
• Partner directly with stakeholders to solve ambiguous problems – Translate real business questions – such as cost overruns, efficiency gaps, and project performance – into clear datasets, metrics, and reporting solutions.
• Support planning and forecasting workflows – Maintain and improve planning and budgeting environments to help align planning, forecasting, and actual performance.
• Standardize KPIs across teams – Work with Finance, IT, Continuous Improvement, Operations, and other functions to define, document, and govern consistent metrics across the organization.

Senior Data 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

• Design, implement, and maintain scalable and reliable data marts and transformation processes
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs and requirements
• Develop and optimize data models and schemas to ensure data is stored efficiently and can be retrieved and analyzed quickly
• Implement ETL processes and frameworks to transform raw data from multiple sources into structured formats suitable for analysis
• Work closely with our internal Data, Architecture, and DevOps teams to ensure the reliability and scalability of data systems
• Monitor and troubleshoot data pipelines, and look for opportunities for continuous improvement
• Implement data quality checks and testing frameworks to ensure accuracy and integrity of financial data.

Data Analytics Engineer – Finance

• Own end-to-end data modeling for assigned business domains
• Build and optimize ETL/ELT pipelines using dbt and Airflow
• Partner with Engineering, Product, and Data Science teams
• Develop scalable abstractions and frameworks
• Design and deliver dashboards and visualization layers

Senior Analytics Engineer – Financial Analytics

• Support the data and analytics team in transforming raw data into meaningful insights
• Assist in building and maintaining data pipelines under guidance from senior team members
• Support data cleaning, transformation, and validation processes
• Help document data sources, transformations, and workflows
• Work with team members to understand basic business requirements
• Assist in developing simple data models
• Support creation and maintenance of dashboards and reports
• Participate in team meetings, code reviews, and knowledge-sharing sessions
• Collaborate with cross-functional teams including IT, HR, Finance, and Operations
• Learn and apply best practices in analytics engineering, data modeling, and data governance.

Analytics Engineer Intern

• Own Landing's dbt models, data pipelines, and warehouse architecture.
• Maintain dbt model library — keeping models clean, tested, documented, and aligned with business data usage.
• Manage Landing's ELT stack — including Stitch, Snowflake, dbt, and associated monitoring and orchestration tooling.
• Establish a finance and operations data spine — authoritative source of truth for core executive KPIs, metric definitions, and warehouse logic.
• Develop analytics engineering standards, tooling decisions, and long-term roadmap for data platform.
• Foster cross-functional data partnerships with Engineering, FP&A, and Accounting — translate between technical infrastructure and business need.
• Enforce data quality standards, testing frameworks, and metric governance practices.
• Support executive, board, and investor reporting — work directly impacts high-stakes decision-making and fundraising.

Senior Analytics Engineer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Design and implement complex data pipelines and ETL processes to support cyber investigative capabilities across multi-classification domains
• Architect and develop ETL workflows for highly sensitive data in classified environments
• Support IC Data Services requirements by integrating various intelligence data sources and systems
• Develop and maintain data analytics solutions for desktop and web-based visual analytic applications
• Establish applications that produce manageable, actionable intelligence from streams of structured and semi-structured data
• Design strategies for enterprise database systems and set standards for operations, programming, and security
• Construct and optimize large relational databases across multi-enclave environments (Unclassified, Secret, and Top Secret)
• Tune performance of large-scale data workflows, ensuring cost efficiency, low latency, and high availability
• Design and manage Elasticsearch/Opensearch clusters for fast search, indexing, and retrieval of large-scale datasets
• Integrate new systems with existing warehouse structures and refine system performance and functionality
• Implement CI/CD pipelines for data systems, automate monitoring/alerting, and enforce infrastructure-as-code practices
• Provide technical leadership and mentorship to other team members
• Participate in Program Increments (PIs) and Agile Release Train (ART) activities

ETL Engineer – SME

• Take ownership of meaningful metrics and data products, from definition through adoption.
• Build and maintain dbt models that define how we measure the business.
• Design and maintain dashboards used by growth, finance, and operations to make day-to-day decisions.
• Improve data quality through testing, monitoring, and validation.
• Enable self-serve analytics so teams can answer their own questions and move faster.

• dbt at Fora: project structure, conventions, CI/CD, tests, contracts, documentation, and performance.
• Data models across bronze, silver, and gold layers.
• The semantic layer (dbt MetricFlow or equivalent): metric definitions, dimensions, governance, and adoption.
• Production operation of the transformation layer: jobs, dependencies, failures, retries, alerts, environments, and release hygiene.
• Python tooling for validation, dbt utilities, lightweight automation, and integrations.
• Data quality, test coverage, and observability for modeled tables.
• Partnership with Risk & Analytics to formalize business metrics currently spread across SQL, Tableau, and analyst knowledge.

Senior / Staff Analytics Engineer

• Analyze sales, repair, and upgrade performance data to identify trends, opportunities, and risks across the US & Canada field organizations
• Design and develop dashboards, reports, and data visualizations that translate complex data into clear, actionable insights for Sales, Field Operations, and Leadership
• Develop working fluency in the elevator business: unit profiles, component knowledge, contract basics, and the KPIs that drive rep behavior
• Build and maintain data models that support long ‑ term strategy for repair revenue growth, customer conversion, and sales productivity
• Conduct deep ‑ dive analytics on customer behavior, portfolio performance, and service history to recommend targeted upgrade and repair strategies
• Support business case development for new repair programs, pricing initiatives, and process improvements using data ‑ driven insights
• Ensure data accuracy, governance, and integrity across multiple sources
• Contribute to metadata documentation efforts
• Evaluate business processes to identify areas for AI workflow optimization

Business Analytics Engineer

• Build, maintain, & optimize ETL data pipelines
• Build, maintain, & optimize corporate semantic models
• Address IT tickets & troubleshoot system performance
• Perform administrative duties to maintain the current data environment
• Assist, as needed, with the development of reports and other solutions
• Work with stakeholders throughout solution lifecycle
• Improve platform stability, data value & quality, and promote a data culture
• Actively collaborate with teammates to share workloads, develop skills, & strengthen the team

• Maintain and extend existing data mart builds: claims aggregation, financial metrics, clinical metrics, and curated analytical tables that power Innovu’s SaaS platform
• Author, test, and maintain metric SQL templates against Innovu’s canonical data model (medical claims, pharmacy claims, eligibility, provider, plan design)
• Build and maintain data pipelines in Python and SQL (Redshift, PostgreSQL, S3-based integrations)
• Profile and validate third-party data feeds during vendor onboarding (new data sources land regularly)
• Investigate data quality issues from warehouse transformation logic through final output
• Produce analytical deliverables for clients and internal stakeholders (PMPM trending, cohort analysis, cost drivers)
• Write clear documentation for metric definitions, data lineage, assumptions, and known limitations
• Participate in monthly rotation for support triage (incoming tickets), release management, and mart build monitoring
• Participate in code reviews and contribute to team standards

• Design and implement advanced data visualizations and interactive dashboards that transform complex data into clear, actionable insights, applying strong user experience principles to drive understanding and data-driven decision-making.
• Lead the adoption and integration of Augmented Analytics capabilities, including AI/ML-driven insights, natural language query (NLQ), and natural language generation like “Talk to your Data” features within enterprise BI platforms.
• Lead and evolve self-service analytics (BIaaS) enablement frameworks, providing training, guidance, and reusable assets to empower business users and improve data literacy across the organization.
• Drive enterprise adoption and engagement through Analytics User Communities (Power BI/Tableau User Group etc.), incorporating feedback to continuously improve analytics capabilities and user experience.
• Own and manage the Analytics Service Catalog, ensuring user requests are fulfilled through standardized, governed, and scalable solutions aligned with enterprise priorities.
• Lead modernization efforts by migrating legacy BI assets to enterprise platforms (Power BI/Tableau), improving performance, scalability, and alignment with enterprise data strategy.
• Establish and maintain governance practices for visualization assets, including certification, reuse, lifecycle management, and adherence to data standards.
• Maximize the value of BI solutions by ensuring strong user engagement and platform reliability — tracking usage and performance, managing access/licensing, and providing proactive support to maintain solution integrity.
• Identify opportunities for optimization, automation, and innovation across analytics platforms, user experience, and data consumption patterns.
• Establish and enforce data visualization standards and best practices to ensure consistent, high-quality, user-centric analytics experiences across platforms.
• Provide subject matter expertise in BI and analytics, guiding Agile project teams with technical leadership to drive successful solution delivery.
• Mentor team members and others through Job Rotation/Shadow or skill up program and promote best practices in data visualization, storytelling, and analytics design.

Senior Data Visualization, Augmented Analytics Engineer

• 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.

Data Analytics Engineer

• Support the Data Integration and Analytics (DI&A) Team virtually
• Focus on supporting Engineering & Technology Innovation business organization
• Design and develop database systems to integrate data from numerous sources
• Improve data quality and support clean and efficient data mining and management
• Maintain database security architecture
• Develop and map XML schema to support data import/export with Oracle database
• SQL coding and PowerBI expertise
• Establish related data tables, views, constraints, triggers, stored procedures, and functions using T-SQL and PL/SQL
• Utilize PL/SQL and JavaScript within Oracle Application Express (APEX)
• Understand complex data analysis and mining solutions

Data Integration and Analytics Developer

• 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 and maintain production-grade Microsoft Fabric semantic models that serve as the single source of truth for enterprise metrics
• Define, document, and enforce KPI and metric definitions in collaboration with operational and finance stakeholders
• Build clean, reusable DAX measures and optimized data model relationships across fact and dimension structures
• Design and deliver analysis-ready data structures that translate warehouse data into formats optimized for reporting and decision-making
• Partner with data engineers to align pipeline outputs with semantic layer requirements, ensuring data arriving from upstream processes meets the structure and quality needed for governed reporting
• Establish and enforce naming conventions, measure governance standards, and row-level security patterns
• Audit available operational datasets against business decision needs, identifying gaps and defining requirements for upstream pipeline development.
• Manage the lifecycle of metric definitions including versioning, deprecation, and change communication to downstream report consumers.
• Document semantic models and provide guidance to Power BI developers consuming them
• Stay current with the Microsoft Fabric and Power BI ecosystem and help shape platform evolution.

• The Senior BI Analytics Engineer is a core technical role in Jamf’s data organisation.
• You’ll own the design and delivery of trusted data models across the Jamf Data Platform, from source-aligned staging layers through to the business-facing marts and semantic views that power BI, financial reporting, and product analytics.
• You’ll bridge the gap between raw data from backend services and the decisions Jamf needs to make faster.
• Work closely with data engineers, analysts, data scientists, and business stakeholders to ensure data is accurate, well-defined, and genuinely useful.
• Raise the bar on data quality, drive best practices across the discipline, and help make the Jamf Data Platform a genuine competitive asset.
• You may be required to work periodically at a Jamf office or collaborative work location with other Jamf employees in your area for certain events or moments that matter.

Senior Business Intelligence Analytics Engineer

• Design, develop, and maintain scalable ETL/ELT pipelines that support cross-functional data needs
• Build, deploy, and optimize analytical dashboards and other reporting tools to support strategic decision-making
• Write well-structured, testable code in Python, R, and SQL to support analytics workflows
• Lead the development and best practices of version control, documentation, and coding standards
• Partner with data scientists to bring analytics products from prototype to production
• Identify and integrate AI-driven tools to enhance data processing and reporting capabilities
• Mentor junior team members and contribute to a culture of knowledge sharing and continuous improvement

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