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

• Conduct deep dive analysis and develop internal reports and dashboards to inform key product and business decisions
• Partner with Forward Deployed Engineers and AI Product Specialists to provide data-driven insights that inform how AI agents are built, tuned, and evaluated for individual customers
• Inform how key metrics should be calculated and presented in the Regal app
• Use AI coding agents to accelerate analysis and data discovery
• Create and maintain documentation of business metrics and the data model

• Develop clean, well-structured data models that support business operations, executive reporting, and advanced analytics.
• Partner with functional stakeholders to translate business problems into scalable, actionable data solutions.
• Establish a semantic layer, documentation standards, and freshness SLA.
• Ensure data accuracy, quality, and compliance through governance, observability, and monitoring best practices.
• Improve data architecture and storage solutions to enhance performance, reduce costs, and optimize batch and real-time workflows.
• Contribute to PHI-adjacent modeling work with best practices on governed data separation.
• Own the ongoing reliability and evolution of production data products, driving projects from concept to execution.
• Operate with initiative and autonomy in a fast-paced, mission-driven environment.

• Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide financial analytics and decision-making.
• Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes financial and operating metrics, including revenue, retention, customer growth, usage, margin, and forecast inputs.
• Partner cross-functionally with Product, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics (ARR, NRR).
• 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.
• Lead data governance initiatives, ensuring high standards of data quality, consistency, documentation, and access control across the analytics ecosystem.
• Structure financial metric definitions, business logic, and accounting context in ways that can support AI-assisted reporting, natural language analytics, and automated anomaly detection.

Senior Analytics Engineer, Finance

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Own the dbt project(s). Maintain, evolve, and review pull requests across our dbt codebase in Snowflake. You'll be a code owner on the repo and will uphold and improve our SQL style, materialization patterns, incremental model conventions, and layered architecture.
• Orchestrate the analytics pipeline. Manage our production dbt jobs and their SLAs (currently targeting 7AM ET freshness), monitor runtimes in Metaplane, and proactively tune orchestration to keep jobs healthy as the data volume grows.
• Define and defend shared metrics. Be the authority on key metric definitions and ensure analysts, dashboards, and downstream models are using the canonical source. Participate in definition discussions and ensure decisions are documented.
• Drive documentation culture. Build and own our Data tables documentation wiki and dbt model documentation. Write runbooks for incident response, onboarding guides, and contribution standards so the team can operate confidently and we can easily provide context for various genAI tools
• Lead data quality and observability. Manage Metaplane monitors, triage alerts, and maintain data contracts with downstream consumers. Debug anomalies end-to-end — from ingestion to the dashboard.
• Deprecate the old, build the new. Continuously identify models that no longer serve us, shepherd them through deprecation with proper signoff, and clean up corresponding warehouse tables and monitors.
• Support compliance and vendor management. Handle audit and compliance reviews involving our dbt and BI tooling (Upbound/SOX), manage our dbt contract renewals, and track consumption-based usage.
• Mentor the broader data team. Run code reviews with domain analysts, establish review coverage by product area, and help analysts grow their dbt contribution skills

Senior Analytics Engineer

• Own the Domo platform: administration, user management, governance, and performance optimization.
• Design, build, and maintain dashboards, cards, and data applications that serve business stakeholders across departments.
• Ensure all reporting is accurate, consistent, and aligned with defined KPI standards.
• Develop and enforce best practices for dashboard design, naming conventions, and content organization within the platform.
• Build and maintain data ingestion workflows that bring data into Domo from source systems.
• Monitor data jobs and ingestion schedules, investigate issues, and coordinate with data engineers to resolve failures.
• Apply last-mile SQL transformations within Domo to shape, filter, and prepare data for dashboard consumption.
• Serve as the primary point of contact for all business intelligence requests and questions from stakeholders.
• Partner with business teams and data analysts to understand reporting needs and deliver solutions.
• Enable self-service analytics by training business users on Domo features, dashboard navigation, and data interpretation.
• Ensure uniform KPI definitions and calculations across all dashboards and reports.

BI/Analytics Engineer

• 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

• 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 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.

• Own the entire data layer downstream of production databases — warehouse, dbt models, Airbyte pipelines, orchestration — plus primary company-wide dashboards, data access patterns for AI agents, and an advisory role on data modeling in external systems (e.g., HubSpot) to keep upstream data clean.
• Teach data literacy across the company — how to think about metrics, write better queries, and self-serve in Hex. Best practices for structuring data in the tools teams own. Metrics definitions and consistency so the team asks the right questions of the data.
• Improve the data model. Grow our capacity by resolving gotchas, improving documentation, and building trust. Enable teams to self-serve on reliable data.
• Get deep into Ambrook's business model, product, and customers to understand how we make money and who we serve.
• Trace the full data lineage from product databases through Airbyte into the warehouse to understand the plumbing and where things can break.
• Audit the existing warehouse schema, dbt models, and Hex dashboards; document the known gotchas and tribal knowledge.
• Meet with Growth, Ops, CS, Product, and Engineering to understand their data needs and pain points.
• Own the primary company-wide dashboards — have audited, corrected, and taken accountability for the core dashboards the whole company relies on.
• Begin evolving the data model — started refactoring dbt models to address the highest-priority gotchas and quality issues identified in month one.
• Establish a plan for agent data access — define clean naming conventions, clear data access patterns, and a recommendation on what level of data LLMs should be able to query (raw vs. curated semantic layer.)
• Stood up initial systems that enable teams to self-serve with confidence. The team has processes and tools to unblock themselves with occasional intervention.
• Deliver a significantly improved data model with well-documented models and a meaningfully more trustworthy warehouse.
• Have agent data access operational or in progress with a clean semantic layer or access patterns in place so LLMs can pull data reliably.
• Actively educate the team on data concepts through documentation, 1:1s, and workshops so the team becomes more data-literate and self-sufficient.
• Shape the future of the data function — actively evaluate how AI is changing the data stack and make recommendations on how Ambrook's infrastructure should evolve.

Founding Analytics Engineer

• Own the data model architecture, ensuring core data entities are structured for high-quality context
• Drive speed-to-insights by modeling data into usable, scalable formats
• Build and maintain systems, ensuring high standards for style and maintainability
• Enable self-service analytics across the company with a well-maintained data catalog
• Partner with the business to drive outcomes and translate business requirements into technical solutions
• Lead the evolution of descriptive to predictive analytics, working with AI tools

• Own the Semantic Layer: Build and maintain data models that define our core business metrics — from clinical outcomes to revenue to utilization.
• Orchestrate & Monitor: Run dbt orchestration with integrated alerting, CI/CD pipelines, and data quality testing to catch issues before they reach dashboards.
• Build Reverse ETL Pipelines: Stand up the data flows that feed clean, modeled data back into production systems — population cohort definitions, marketing intelligence, and operational features that depend on well-modeled warehouse data.
• Own Data Compliance: Implement and maintain access controls for PHI data, ensuring the data layer meets HIPAA requirements while remaining accessible.

• 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.

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