Note: The job is a remote job and is open to candidates in USA. Tango Analytics helps businesses make smarter decisions through technology, data, and collaboration. The Staff Analytics Engineer will serve as a hands-on technical anchor, building Tango's governed semantic layer, Redshift data models, and Omni-based customer and internal analytics. The role will also lead the Oracle BI migration, improve AI data reliability, establish engineering standards, and mentor teams as a senior individual contributor.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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 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
Skills
- 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
- 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
- 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
- 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
- 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
Benefits
- Health, dental, and vision insurance
- A 401(k) plan with company match
- Flexible work arrangements, including remote, hybrid, or in-office work
- An inclusive and collaborative culture where diverse perspectives are valued, teamwork is encouraged, and everyone has a voice
Company Overview