Senior Analytics Engineer

Quick Facts

• Role: Senior Analytics Engineer (Analytics Engineering / Data Engineering)

• Focus: End-to-end data foundations for a business domain, including Gold-layer models, semantic layer, metric standards, and self-serve analytics

• Work style: Office-centric hybrid (Mon, Tue, Thu in office; work from home on Wed; Fri depending on teams)

Description

As a Senior Analytics Engineer, you own the curated data foundations for a business domain end to end—turning raw data into reliable, business-ready datasets that teams trust and use. You design Gold-layer dimensional models, implement canonical business logic for core KPIs, and build the semantic layer and Genie spaces so stakeholders can query governed data in plain language using Claude and Databricks Genie. You also manage the metric dictionary, data contracts/SLA at the Silver → Gold boundary, and the reliability of your domain’s dashboards and reporting.

Responsibilities

• Own the Gold layer for a business domain; design and continuously improve dimensional models used by dashboards, Genie spaces, and ELT reporting

• Implement canonical business logic behind core KPIs using governed, versioned metric marts

• Build and curate the semantic layer and Genie spaces (metadata, documentation, prompt/metric definitions) for self-serve querying

• Maintain a single source of truth metric dictionary for definitions, ownership, and locations; ensure consistency across overlapping domains

• Author data contracts and SLAs at the Silver → Gold boundary; collaborate on inputs and own data quality, freshness, and on-call for Gold/metric-mart failures

• Build and maintain certified, board-ready dashboards on governed Gold data; partner with Data Science to productize trusted, reusable datasets

• Partner across Product, Business, Data Science, and Engineering to translate ambiguous questions into scalable datasets and raise data-model quality

Requirements

• 4+ years in analytics engineering, data engineering, or a closely related analytics role; proven ability to independently own decision-critical data models

• Advanced SQL and strong data modeling fundamentals (dimensional modeling, star/snowflake schemas, slowly changing dimensions, semantic layer design)

• Hands-on experience with dbt (or equivalent) and orchestration (e.g., Airflow), plus Git version control

• Experience with modern warehouse/lakehouse platforms (Databricks preferred)

• Experience with data quality testing and observability, schema management/data contracts, and performance/cost tuning

• Domain fluency in at least one business area (e.g., PLG funnel, SLG pipeline, marketing attribution, product telemetry, revenue/ARR)

• Strong cross-functional partnership skills for requirements, prioritization, documentation, enablement, metric-definition alignment, and explaining technical tradeoffs

• Curiosity about AI-native analytics (NL2SQL, metadata/semantic layers for self-serve, tools like Claude and Genie); exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation tools is a plus

Benefits

• Generous, transparent, and fair compensation (base salary and RSUs)

• Health insurance with dental and travel coverage (Lux Med)

• Vacation allowance; career growth budget

• Home office setup budget; gym/fitness card

• Fertility healthcare and family-forming support (Carrot)

• Mental health support (Modern Health)

• Group life insurance

• MacBooks with necessary accessories

• Breakfast and lunch catering on in-office days

• Contract of employment option related to author’s rights usage (where applicable)