Senior Analytics Engineer

About this role

Who We Are

Digible is a privately owned and operated digital marketing company founded in 2017 with a mission to bring cutting-edge solutions to the multifamily industry. We offer a full suite of digital services, alongside Fiona, our predictive analytics platform—the first of its kind.

At Digible, we take pride in our collaborative, transparent, and authentic culture. Since 2021, we've been recognized as a Top Workplace in Colorado and secured the #8 spot in the Best Places to Work Multifamily rankings. From our hiring process to our All Hands meetings and Town Halls, our values are at the core of everything we do.

We believe diversity fuels innovation, and we strive to create an inclusive environment where everyone can bring their authentic selves to work. If you're ready to do the best work of your career, we'd love to have you on the team!

Core Values

• Authenticity — The commitment to be steadfast and genuine with our actions and communication toward everyone we touch.

• Curiosity — The belief that a deep and fundamental curiosity (the "why") in our work is vital to company innovation and evolution.

• Focus — The collective will to remain completely devoted and ultimately accountable to our deliverables.

• Humility — The recognition and daily practice that "we" is always greater than "I".

• Happiness — The decision to prioritize passion and love for what we do above everything else.

The Role

Digible is looking for a Senior Analytics Engineer to join our team!

Our Data team owns the platform and analytics that power Fiona and the decisions made across Digible — from ingestion, through a governed semantic layer, to the BI our ~1,000 multifamily clients and internal teams rely on. The team sits within a ~20-person technology department and partners closely with Product, Engineering, and business stakeholders.

This is a deeply hands-on role at the center of our data warehouse strategy. You'll own the Silver and Gold layers of our medallion architecture, build and govern the semantic layer that gives every metric a single, authoritative definition, and shape the BI tooling and standards our stakeholders rely on every day. Where our Data Engineers own ingestion and the Bronze layer, you own the path from modeled data to decisions — making our numbers consistent, our warehouse fast and cost-efficient, and our analytics self-serve.

You'll report to the Director of Data, partner with Data Engineering on the Bronze-to-Silver handoff, and work with analysts and business stakeholders to translate their questions into trustworthy models and metrics.

If you live in SQL and dbt, care deeply about metric integrity and warehouse performance, leverage AI to accelerate your work, and believe data quality is a product — we'd love to meet you.

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 da

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