Our team builds and operates a
modern cloud-based analytics platform for
Commercial Real Estate (CRE) organizations. The platform delivers scalable, high-performance analytics that transform complex operational data into actionable insights for workplace and real estate teams.
Designed for
rapid deployment, strong data governance, and enterprise-grade security, the platform integrates data from multiple enterprise systems and provides near real-time visibility into business operations. Our mission is to empower organizations with
reliable analytics and intelligent insights that drive better decision-making, operational efficiency, and strategic workplace initiatives.
Key Responsibilities
- Design, build, and maintain semantic models using Databricks Metric Views (or equivalent semantic layer tooling), ensuring metrics are consistent, reusable, and governed across the organization.
- Develop a deep understanding of the underlying data layer — source systems, ingestion pipelines, lakehouse/medallion architecture (bronze/silver/gold) — and how it maps to business-facing metrics.
- Translate business requirements into technical specifications, working closely with analysts, product managers, and business stakeholders to define KPIs, dimensions, and metric logic.
- Write complex, optimized SQL for data transformation, aggregation, and validation across large-scale datasets.
- Build and maintain data pipelines and transformation logic using Python and/or Java.
- Ensure data quality, consistency, and performance of semantic models through testing, validation frameworks, and monitoring.
- Collaborate with data engineering teams to resolve upstream data issues that affect metric accuracy.
- Document metric definitions, data lineage, and semantic model architecture for cross-team consumption.
- Participate in code reviews, design discussions, and contribute to engineering best practices.
- Troubleshoot and optimize query performance across the semantic and data layers.
- Operate within our AI-Driven Development Lifecycle (ADLC), using agentic coding tools (e.g., Claude Code or similar) as the primary means of writing, testing, refactoring, and documenting code — from requirement to deployment.
- Design effective prompts, task breakdowns, and review checkpoints to direct agentic tools reliably, while retaining full accountability for code correctness, security, and architecture.
- Continuously evaluate and adopt emerging agentic development workflows and tooling to improve engineering velocity and quality.
Required Qualifications
- 3–6 years of professional software/data engineering experience.
- Hands-on experience with Databricks, including Metric Views, Unity Catalog, Delta Lake, and Lakehouse architecture.
- Strong proficiency in SQL — including complex joins, window functions, CTEs, query optimization, and performance tuning.
- Solid understanding of data modeling concepts (star schema, dimensional modeling, fact/dimension tables, slowly changing dimensions).
- Working proficiency in Java and/or Python for building data pipelines, automation, or backend services.
- Demonstrated experience translating business requirements into technical/data solutions.
- Understanding of semantic layers / metric layers (e.g., dbt Semantic Layer, LookML, Metric Views, or similar) and why they matter for consistent reporting.
- Familiarity with version control (Git) and CI/CD practices.
- Strong analytical and problem-solving skills, with attention to data accuracy and detail.
- Comfortable operating in an agentic, AI-first development environment — hands-on experience with agentic coding tools (e.g., Claude Code, GitHub Copilot, Cursor, or similar) for day-to-day development, testing, and code review.
