Senior Analytics Engineer, Databricks Lakehouse

Job not on LinkedIn

🔥 3 minutes ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Compass

WebsiteLinkedInAll Job Openings

10,000+ employees

🏠 Real Estate

📱 Media

Real Estate • Media

Compass is a real-estate-focused content and services site that provides detailed market analysis, buying/selling/renting guides, mortgage and financing information, and home improvement and renovation advice. The site offers resources for homebuyers, sellers, renters, agents, and real estate investors — including articles on appraisals, affordable housing, investment strategies, staging and property maintenance. Compass aims to help users make informed decisions across the housing lifecycle through timely market updates and practical how-to content.

📋 Description

• Map and inventory legacy DW consumers (BI, reports, applications and system integrations), classifying by domain and criticality; • Model consumption marts by domain in the gold layer, derived from the silver layer, following the medallion template and data contracts; • Execute the repointing of dashboards, reports, applications and system integrations to the gold layer, wave by wave, coordinated with domain cutovers; • Validate consumption parity between legacy and gold and support the decommissioning of legacy consumption points; • Contribute to the prioritization of waves together with the Product Owner and the Data Architect.

🎯 Requirements

• Analytics Engineering: strong experience in the consumption layer — dimensional modeling/marts, semantic layer and certified metrics; • Databricks: production Spark SQL, Delta Lake and medallion architecture (bronze, silver, gold); • BI repointing (Power BI, Tableau or similar): changing sources, refactoring datasets and validating numeric parity; • System data integrations: APIs, batch extractions and loads between systems, including interface contracts and rollback plans; • Advanced SQL: reconciliation techniques and large-scale dataset comparisons; • Unity Catalog (lineage, discovery and documentation of metrics and consumption models); • Dimensional modeling (Kimball/star schema) and best practices for domain marts; • Python and PySpark for automating parity validations and repointing routines; • Data Contracts and Data Quality (expectations, DQ gates) applied to the consumption layer; • Experience in DW migration with legacy/new coexistence, wave-based cutovers and high-volume dataset reconciliation — important differentiator; • Experience with Data Mesh projects (domains, data as a product, federated governance) — important differentiator; • Knowledge of DataStage or other legacy ETL tools (helps to understand current consumption); • Experience with semantic layers and metric catalogs (metric stores, semantic layers); • Data observability and quality monitoring in the consumption layer; • Experience in the financial or credit sector; • Databricks certifications (Data Analyst Associate, Data Engineer Associate); • Open data contract specifications (Open Data Contract Standard, ODPS);