Role Description We’re hiring our first dedicated analytics engineer to join an established data engineering team. You won’t be building alone — you’ll work alongside data engineers who own the pipelines and infrastructure (Fivetran, Databricks), and your job is to build the analytics layer on top: reliable data models within our medallion architecture (bronze, silver, gold), clean dashboards, and governed metrics that teams across the company can trust and self-serve. This is a hands-on individual contributor role, but collaboration is central to the work. You’ll spend your time writing SQL, building and maintaining gold-layer models in Databricks, and creating dashboards in Omni Analytics — but you’ll also be a regular presence in conversations with Product, CS, Sales, and Marketing, translating their questions into reliable analytics and surfacing insights they wouldn’t find on their own. Strong cross-team communication skills matter as much as technical depth here. What You’ll Do: - Data Modeling & Transformation - Build and maintain dimensional and semantic models in Databricks that serve as the analytics layer for the company, working within a medallion architecture (bronze, silver, gold). - Own the gold layer — write clean, tested, version-controlled SQL transformations that turn curated silver-layer data into business-ready tables optimized for reporting and analysis. - Partner with data engineers on data quality, lineage, and freshness across the medallion layers to ensure models are reliable and well-documented. - Dashboards & Business Intelligence - Design and build dashboards and topics in Omni Analytics for Product, Client Success, Sales, Marketing, and executive stakeholders. - Implement governance practices for dashboards: naming conventions, documentation, version control, and testing. - Create client-facing analytics views that can be shared externally to demonstrate platform value. - Metrics & Governance - Collaborate with Product and leadership to document KPI definitions and implement them consistently across all reporting surfaces. - Contribute to a metric catalog so that stakeholders reference the same numbers from the same source. - Flag data quality issues proactively and work with the data engineering team to resolve them. - Investigations & Analysis - Conduct deep-dive investigations into conversion drops, data mismatches, and funnel issues using SQL and platform tooling. - Document findings and build repeatable analysis runbooks so investigations don’t start from scratch each time. - Partner with product engineering to identify gaps in event instrumentation and recommend improvements. - Stakeholder Enablement - Help internal teams learn Omni Analytics so they can answer routine questions without filing a request. - Write lightweight documentation for dashboards, data models, and common queries. Qualifications - 5+ years in analytics engineering, BI development, or product analytics roles. - Advanced SQL — you write complex, performant queries daily and are comfortable with window functions, CTEs, and large-scale data. - Hands-on experience with a modern BI tool (Omni, Looker, or similar) — building explores, semantic layers, and governed dashboards. - Experience with Databricks, BigQuery, or a comparable cloud analytics platform for data modeling and transformation. - Familiarity with medallion layer architecture (bronze/silver/gold) is strongly preferred. - Familiarity with ELT/ETL orchestration tools (Fivetran, dbt, or similar). Requirements - Product-minded: you don’t just build tables — you understand what questions the business is trying to answer and work backward from there. - Builder mentality: you prefer creating tested, documented, reusable assets over one-off queries. - Clear communicator: you can explain data findings to non-technical stakeholders in writing and in conversation. You’re comfortable presenting to CS, Sales, Marketing, and Product teams and translating between technical and business language. - Cross-team collaborator: you proactively build relationships across departments, seek out context before building, and make sure your work is shaped by the people who will use it — not just the people who requested it. - Comfortable with ambiguity: this is a first-of-its-kind role at Opiniion, and you’ll help shape how analytics work gets done. Nice-to-Haves - dbt or similar modeling/testing frameworks. - Python for light ETL, scripting, or anomaly detection. - Comfortable working with data from MongoDB or other NoSQL sources. - GA4 or product analytics instrumentation experience. - Proptech, real estate technology, or multifamily industry experience. - Experimentation platforms or statistical analysis background. What Success Looks Like - In your first 90 days, you’ll have shipped your first governed dashboards in Omni and earned trust with at least one stakeholder team. - Within 6 months, here’s what we’d expect: - Core business KPIs are modeled in Databricks and surfaced through Omni dashboards that stakeholders actively use. - The majority of routine analytics questions from Product, CS, Sales, and Marketing are answered through self-service dashboards rather than ad-hoc requests. - Data models are documented, tested, and version-controlled — not tribal knowledge. - You’ve conducted at least one meaningful investigation that identified a root cause of a platform or funnel issue and informed a product fix. - Executive dashboards are in production and shared with leadership and clients. Benefits - Comprehensive healthcare plans, encompassing medical, dental, and vision insurance, along with group life coverage. Opiniion covers 40-90% of the premium cost for employees and all dependents. - 401(k) retirement plan with a 100% corporate match on the first 1% and 50% match on the next 5%. - Pre-tax Health Spending Accounts (HSA). - Paid Parental Leave for all new parents (including adoption or foster care). - Unlimited Time Off policies. - 10 Paid Holidays annually. - Monthly Gym Reimbursement benefit.

• Develop and maintain Kin’s Looker semantic layer, including LookML views, explores, and user-friendly dashboards • Partner with domain data engineers to standardize definitions, metrics, and calculations while performing data analysis, validation, and exception reporting across business lines • Support and administer Looker or similar BI platform (permissions, performance tuning, content lifecycle, QA) • Model and maintain dimensional models to support enterprise analytics and reporting • Collaborate with Finance, Marketing, and Product to deliver and maintain high-impact reports and KPIs • Implement data quality monitoring for trusted business reporting • Champion documentation and reusable data patterns for BI development • Partner with the broader data platform team to evolve our Lakehouse and data governance practices

• Deploy & Lead dbt Implementation: Act as the internal champion for dbt. • Architect the Single Source of Truth: Define the core data models that align the entire company. • Fuel AI & Self-Service: Design the Gold Standard tables that will serve as the brain for our Self-Service Platform and AI Assistants. • Build for History: Use dbt’s snapshotting and modeling capabilities to implement historical tracking (SCDs). • Pipeline Optimization: Collaborate with Data Engineering team to optimize AWS Airflow DAGs.

• Deploy & Lead dbt Implementation: Act as the internal champion for dbt. • Architect the Single Source of Truth: Define the core data models that align the entire company. • Fuel AI & Self-Service: Design the Gold Standard tables that will serve as the brain for our Self-Service Platform and AI Assistants. • Build for History: Use dbt’s snapshotting and modeling capabilities to implement historical tracking (SCDs). • Pipeline Optimization: Collaborate with Data Engineering team to optimize AWS Airflow DAGs.