Senior Analytics Engineer

This is not a traditional reporting or dashboarding role. We are hiring a Senior Analytics Engineer to own the analytics layer end-to-end — from understanding raw source data to defining metrics, shaping data models, and delivering trusted dashboards and reporting outputs. This role sits at the intersection of business, analytics, and data engineering. You will translate ambiguous business questions into structured data solutions, working closely with stakeholders and the data engineering team to ensure accurate, scalable, and trusted analytics. You will be responsible for how data becomes reporting, not just the final dashboards. What you will own Analytics Ownership (End-to-End) - Own analytics and reporting requirements across the business - - Translate business questions into structured data requirements and outputs - - Decide whether needs should be solved via dashboards, reports, datasets, or ad hoc analysis - Data Understanding & Modeling - Investigate and document data sources, reports, fields, and metric definitions - - Map source data into existing data models (fact and dimension tables) - - Define and maintain business logic and metric calculations - - Ensure alignment between business definitions and data structures - Partnership with Data Engineering - Work closely with Data Engineering to define: - - Transformations - - Data models - - Data exposure layers - - Validation requirements - - Contribute to improving data pipelines and modeling approaches - Dashboard & Reporting Ownership - Own dashboards across operational, executive, and ad hoc reporting - - Ensure dashboards are accurate, usable, and trusted - - QA outputs against source data and business expectations - Data Quality & Validation - Reconcile discrepancies across systems and reports - - Build validation workflows and ensure ongoing data integrity - - Identify gaps in data definitions, reporting logic, and processes - Stakeholder Partnership - Partner with business stakeholders to define KPIs and reporting standards - - Communicate data logic clearly across technical and non-technical audiences - - Build trust in reporting outputs through clarity and consistency - What success looks like - Metrics are clearly defined, documented, and consistently applied - - Stakeholders trust dashboards without questioning underlying data - - Data discrepancies are proactively identified and resolved - - Reporting evolves from reactive to structured and scalable - - The analytics layer becomes a reliable bridge between business and engineering - Requirements Core Technical Skills - Advanced SQL (joins, aggregations, validation logic, metric building) - - Experience working with modern data warehouses (BigQuery preferred) - - Strong understanding of data modeling concepts (fact & dimension tables) - - Experience with BI tools (Looker Studio preferred) - Analytics Engineering Exposure - Familiarity with Dataform (SQLX, workflows, or similar tools like dbt) - - Ability to read and understand transformation logic - - Understanding of ETL/ELT concepts and data pipelines - Programming & Tools - JavaScript (for Dataform configs or similar use cases) - - Python (for analysis, validation, or tooling) - - Git / GitHub - Additional Tools (Nice to Have) - D3.js, Streamlit - - Google Apps Script - - Cloud Run - Experience - 4+ years in data analytics, analytics engineering, or BI roles - - Experience working with messy, real-world operational data - - Healthcare, EHR, or CRM data experience is a strong plus - Important Clarification This role is NOT for candidates who: - Only build dashboards from clean, pre-modeled data - - Focus purely on visualization tools (e.g., Power BI-only profiles) - - Lack hands-on experience working directly with raw or semi-structured data -