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Let's Tango. Where Innovation Meets Impact. At Tango Analytics, we're all about helping businesses make smarter decisions through powerful technology, insightful data, and a whole lot of collaboration.

Whether you're a creative thinker, a strategic planner, a tech wizard, or a customer champion, there's a place for you on our team. We believe work should be meaningful and fun - so if you're ready to make a difference while enjoying the journey, come join us and let's Tango. Role Summary: We are looking for a Staff Analytics Engineer to be a high-level individual contributor on Tango's Analytics Engineering team and the hands-on technical anchor for the modern analytics foundation we are building.

You will design and build the governed semantic layer, the curated data models on our Redshift warehouse, and the Omni-based analytics that both our customers and our internal teams rely on. You will be prolifically hands-on: writing the models, defining the metrics, and setting the patterns the rest of the team builds on. This role is central to our transformation.

Tango's analytics have historically been delivered the old way: bespoke, services-led custom reporting on a legacy Oracle BI stack. We have signed Omni as our new analytics layer and are building a modern data warehouse on Redshift underneath it. You will be the engineer who makes the new stack real, extracting business logic from legacy reports, re-implementing it as tested, reusable models, and standing up the semantic layer that makes every metric consistent across dashboards, embedded product surfaces, and AI.

Key Responsibilities: Build and own the governed semantic and metrics layer Design and own the semantic layer that is the single source of truth for Tango's metrics. Define metrics once, in code, so they resolve consistently across every dashboard, embedded product surface, notebook, and AI query. Establish the modeling conventions, certification process, and documentation that keep the layer coherent as it grows and as more teams and agents consume it.

Model Tango's data on the modern warehouse Design and build curated, well-documented, tested data models on Redshift that turn a deep and complex real estate and facilities data model into analytics-ready datasets. Bring analytics-engineering discipline (version control, testing, CI/CD, lineage, and performance and cost awareness) and make it the standard the team works to. Ship embedded, customer-facing analytics on Omni Build the embedded, self-serve analytics experiences that live inside Tango's product modules, replacing static exported reports with dynamic analytics customers can explore themselves.

Handle multi-tenant isolation, performance, and per-customer variation as first-class engineering concerns so the experience is fast, correct, and safe across the base. Make the data layer trustworthy for AI Structure the semantic definitions, documentation, and lineage so that natural-language querying and Tango's agents return correct, consistent answers rather than plausible guesses. Build the evaluation and quality checks that tell us when a model or metric is trustworthy enough for an agent to stand on, and treat the analytics layer as first-class, machine-readable context for AI.

Lead the Oracle BI to Omni migration, hands-on Be the technical anchor of the migration off the legacy Oracle BI stack. Reverse-engineer and extract the business logic buried in existing reports, re-implement it as governed models and metrics, and drive a disciplined parallel-run so customers and internal consumers never lose the reporting they depend on during cutover. Set the standard and level up the team Create the reusable patterns, libraries, and review practices that make the whole team faster and more consistent.

Mentor engineers and contractors as they move from legacy BI to modern analytics engineering, raise the craft bar through code and design review, and lead by example without needing a management title to do it. Partner across platform, product, and consumers Partner closely with the Data Platform team on the warehouse and pipelines you build on, with the domain product teams adopting your analytics surface, and with the ML and agent teams consuming your semantic layer. Create clarity in ambiguous, cross-team situations by proposing options, decisions, and timelines, and help sequence the work into incremental, shippable phases.

Required Skills: Data stack 8 or more years in analytics engineering, data engineering, or business intelligence, with deep, hands-on experience on a modern data stack and a sustained track record of building business-critical data and analytics systems. Expert SQL and strong dimensional and semantic data modeling, with deep experience in a transformation framework such as dbt and a governed metrics or semantic layer (for example, dbt Semantic Layer / MetricFlow, Cube, LookML, or Omni's modeling layer). Strong command of a cloud data warehouse, ideally Amazon Redshift or Snowflake, including performance and cost optimization at scale.

Analytics-engineering discipline as second nature: version control, testing, CI/CD, documentation, and data lineage. Embedded and customer-facing analytics Hands-on experience building embedded, multi-tenant, customer-facing analytics inside a SaaS product, including self-serve dashboards and reporting that customers use, with strong attention to tenant isolation, performance, and usability. Experience migrating off a legacy BI platform (Oracle BI / OBIEE / Oracle Analytics, Cognos, Crystal Reports, SSRS, or similar) is a strong plus.

AI-forward instinct A real, current understanding of how AI consumes analytics: why a governed semantic layer, clean metric definitions, documentation, and lineage are what make natural-language-to-SQL and agents reliable, and how to build and evaluate for that. Fluency using AI tooling in your own workflow to accelerate modeling, documentation, and development. Technical leadership as an individual contributor A track record of setting technical direction, establishing patterns and standards, and leveling up other engineers through mentorship and review, driving impact across squads without formal management responsibility.

Pragmatic judgment about tradeoffs between speed, quality, cost, and performance, and clear communication with technical and non-technical partners alike. Bachelor's or Master's degree in Computer Science, Data, Statistics, Engineering, or a related field, or equivalent practical experience. Nice to have Prior experience in real estate technology, facilities management, workplace technology, IWMS, or vertical SaaS.

Direct experience with Omni, with Redshift, or with dbt in production. Familiarity with deploying analytics in compliance-bound environments such as FedRAMP and SOC 2. What We Offer We're committed to creating an environment where you can thrive-professionally and personally.

Our offerings include: Competitive Compensation We recognize and reward your contributions with a salary package that reflects your value. Comprehensive Benefits Including health, dental, and vision insurance, a 401(k) plan with company match, and generous paid time off to support your well-being. Flexible Work Environment Whether remote, hybrid, or in-office, we support work arrangements that promote productivity and balance.

Inclusive & Collaborative Culture We foster a workplace where diverse perspectives are valued, teamwork is encouraged, and everyone has a voice. Tango is proud to be an equal opportunity employer. We are committed to equal opportunity regardless of race, ethnicity, religion, parental status, sexual orientation, age, citizenship, disability, or veteran status.

Base pay offered is contingent on qualifications and other operational considerations. Base pay is just one piece of the full compensation structure offered at Tango. If this pay range is outside of your expectations, we still encourage you to apply and have a conversation with us.

Base pay offered for this position is: $160,000 - $190,000 *Applicants must be authorized to work in the U.S. for any employer. *We cannot sponsor employment-based visas at this time.