Senior Analytics Engineer

You'll Love This Job If You
Embrace Digible's core values: authenticity, curiosity, focus, humility, and happiness
Enjoy writing production-grade SQL and dbt models, and think in terms of clean, layered, well-tested transformations
Have experience across the modern data stack (we use dbt, Snowflake, Fivetran, Prefect) and an opinion on where it should go next
Believe a metric should be defined once and trusted everywhere — and get satisfaction from killing metric drift and duplicate definitions
Care about warehouse performance and cost as a first-class concern, not an afterthought
Enjoy turning ambiguous stakeholder questions into governed, reusable data products and self-serve BI
Use AI tools as a natural part of your engineering workflow — you see AI as an accelerator for how you build, debug, and deliver
Have an insatiable appetite for learning and always want to be working on your craft
Approach challenges with a customer-first mentality and curiosity
Thrive in ambiguity, leaning on resourcefulness and customer understanding in an open and empathetic culture
Are excited to contribute to team growth through pairing and shared learning
What You'll Do
Drive data warehouse strategy and performance — shape our modeling standards, materialization strategy, and query performance; tune for both speed and cost; and help evaluate and execute the direction of our warehouse (Snowflake today, with alternatives under active consideration)
Own Silver- and Gold-layer modeling in dbt — build clean, documented, tested, and governed models, and lead the effort to consolidate redundant pre-materialized views, resolve metric drift, and correct non-additive measures at risk of bad re-aggregation
Build and govern the semantic layer — establish a single source of truth for metric definitions, eliminate divergent measure definitions across models, and keep definitions portable as our warehouse evolves
Lead BI tooling strategy and enablement — standardize our BI stack, build governed data products, and enable trustworthy self-serve analytics
Partner with stakeholders and analysts — translate business questions into durable models and metrics, and establish the best practices, governance standards, and tooling that let upstream teams own their domain data well without it becoming the wild west
Troubleshoot data quality and consistency issues — drive toward root cause and long-term fixes across the transformation and consumption layers
Contribute to platform evolution — identify opportunities to optimize, refactor, or scale our analytics infrastructure, and stay informed on developments in the modern data stack, introducing tools and processes that improve our workflows
How Success Will Be Measured
Core metrics have a single, governed definition in the semantic layer, and metric drift and duplicate or ad-hoc definitions are measurably reduced.
Silver and Gold models are documented, tested, and performant — with warehouse cost and query times flat or improving as data volume and client count grow.
Analysts and business stakeholders self-serve trusted metrics through standardized BI, cutting down on one-off data pulls and disputes over what the numbers mean.
What You Should Have
5-7+ years of data/analytics engineering experience, including at least 2 years in a senior capacity
Expert proficiency with SQL and data modeling tools (dbt, dataform, SQLMesh) for modeling, testing, documentation and macros and strong command of dimensional modeling and medallion/layered architectures
Hands-on depth with at least one cloud data warehouse (Snowflake and/or BigQuery), including performance and cost optimization
Experience with a semantic / metrics layer (e.g., dbt Semantic Layer / MetricFlow, Cube, LookML, or similar) and a track record of standardizing and governing metric definitions
Proficiency with one or more modern BI tools (e.g., Hex, Sigma, Omni, Tableau, Looker, Metabase, Lightdash)
Working proficiency with Python for transformation, tooling, and automation
Strong proficiency with Git and version control practices
Demonstrated fluency with AI-assisted development tools in your engineering workflow
Experience working with modestly-sized, fast-paced teams
Strong communication skills and the ability to partner across engineering, product, and business stakeholders
Working knowledge of iterative, value-focused technical delivery
What Will Set You Apart
Familiarity with digital marketing data or the multifamily/real estate industry
Experience leading or contributing to a data warehouse migration (e.g., Snowflake ↔ BigQuery)
Experience operating a semantic layer or large dbt project at scale, including metric governance and drift remediation
Experience with BI write-back, reverse ETL, or finance-focused analytics
Physical Requirements
Prolonged periods sitting at a desk and working on a computer.
Must be able to lift up to 15 pounds at times.
This role is open to candidates located within the United States.

While this job description outlines the core expectations of the role, it's not a full list of everything you'll do at Digible. We believe in leaning in by hitting your key goals, sharing insights, and finding new ways to elevate performance, process, and client success.

Pay, Perks and More!
Salary Range: $140,000 to $160,000
4-Day Work Week (32-Hour Work Week)
US Remote — Work From Anywhere
Profit Sharing Bonus
3 weeks PTO + Sick Leave + Bereavement
11 paid holidays (not counting ones that fall on a Friday)
401(k) + Match
75% Employer-Paid Health Benefits (Medical, Dental, Vision)
Mental and Physical Wellness Reimbursement ($75/mo each)
$1,000/year travel fund (after 3+ years)
Paid Parental Leave
Dog-Friendly Office
Monthly Social Events
Weekly lunches and snacks for in-office employees

Skills Required
5-7+ years of data/analytics engineering experience, including at least 2 years in a senior capacity
Expert proficiency with SQL and data modeling tools (dbt, dataform, SQLMesh) for modeling, testing, documentation, and macros
Strong command of dimensional modeling and medallion/layered architectures
Hands-on depth with at least one cloud data warehouse (Snowflake and/or BigQuery), including performance and cost optimization
Experience with a semantic/metrics layer (dbt Semantic Layer / MetricFlow, Cube, LookML, or similar) and metric governance
Proficiency with one or more modern BI tools (Hex, Sigma, Omni, Tableau, Looker, Metabase, Lightdash)
Working proficiency with Python for transformation, tooling, and automation
Strong proficiency with Git and version control practices
Demonstrated fluency with AI-assisted development tools in your engineering workflow
Strong communication skills and ability to partner across engineering, product, and business stakeholders
Working knowledge of iterative, value-focused technical delivery