Software Engineer 2

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.