Databricks Platform & Database Engineer

We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings to our customers. We’ve continually refined, transformed and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors. We continue that effort today and are always working to improve and drive innovation. This is how we deliver for our customers, our employees, and investors. By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate. We are looking for a Databricks Platform & Database Engineer with hands-on experience in enterprise Databricks environments on AWS and traditional database engineering spanning relational and non-relational systems. In this role, you will serve as the primary owner of our Databricks environment on AWS responsible for administration, automation, governance, and platform reliability end to end. Alongside that, you will manage, optimize, automate, and govern a broader portfolio of relational databases (SQL Server, Aurora, PostgreSQL, MySQL) and nonrelational data stores across both AWS cloud and on-premises data centers. CoStar is investing heavily in artificial intelligence to accelerate software delivery, improve platform observability, automate research and business workflows, enhance product experiences through intelligent agents and skills, and improve data quality across enterprise systems. Successful candidates will demonstrate a willingness to learn, adopt, and apply AI technologies responsibly to solve complex business and engineering challenges.

- Lead platform upgrades, workspace migrations, and full environment lifecycle management - Monitor, optimize and right‑sizing clusters, SQL warehouses, and job compute for performance and cost; analyze Spark executor logs and query plans to diagnose and resolve bottlenecks - Support data ingestion from relational and non-relational sources into Databricks via AWS, DMS or native Lakeflow ingestion services

- Support data ingestion from relational and non-relational sources into Databricks via AWS, DMS or native Lakeflow ingestion services - Lead the design and implementation of end-to-end data workflows, covering the reliable distribution of curated data to downstream systems including SQL Server, DynamoDB, S3, and other enterprise data consumers. - Design, implement, and administer relational and non-relational databases such as SQL Server, Aurora, PostgreSQL, MySQL, and DynamoDB across AWS (RDS/Aurora) and on-premises data centers

- Lead the design and implementation of end-to-end data workflows, covering the reliable distribution of curated data to downstream systems including SQL Server, DynamoDB, S3, and other enterprise data consumers. - Design, implement, and administer relational and non-relational databases such as SQL Server, Aurora, PostgreSQL, MySQL, and DynamoDB across AWS (RDS/Aurora) and on-premises data centers - Manage provisioning, patching, backups, replication, failover, version upgrades, and cross-platform migrations between cloud and on-premises environments

- Build and maintain CI/CD workflows for platform configuration and pipeline code - Implement proactive monitoring and alerting across Databricks jobs, pipelines, clusters, SQL warehouses, and all database instances - Implement and enforce cluster policies, SQL warehouse governance, and workspace hardening.

- Implement proactive monitoring and alerting across Databricks jobs, pipelines, clusters, SQL warehouses, and all database instances - Implement and enforce cluster policies, SQL warehouse governance, and workspace hardening. - Manage Databricks audit logs, lineage, and compliance reporting

- Deep expertise in Databricks platform administration: Unity Catalog, workspace security, cluster management, and Auto Loader on AWS - Strong relational database engineering skills: schema design, query optimization, indexing, stored procedures, replication, and failover across SQL Server, Aurora, PostgreSQL, and/or MySQL - Advanced SQL and proficiency in PySpark and Python

- Strong relational database engineering skills: schema design, query optimization, indexing, stored procedures, replication, and failover across SQL Server, Aurora, PostgreSQL, and/or MySQL - Advanced SQL and proficiency in PySpark and Python - Infrastructure-as-Code experience with Terraform; CI/CD pipeline management

Responsibilities - Deploy, administer, and maintain Databricks workspaces on AWS, including workspace configuration, network settings, access management, Runtime versions, cluster configurations, libraries, and init scripts across dev, staging, and production - Manage Unity Catalog objects (catalogs, schemas, tables, external locations) and administer workspace security: users, groups, service principals, permissions, and enterprise identity provider integrations

- Monitor, optimize and right‑sizing clusters, SQL warehouses, and job compute for performance and cost; analyze Spark executor logs and query plans to diagnose and resolve bottlenecks - Support data ingestion from relational and non-relational sources into Databricks via AWS, DMS or native Lakeflow ingestion services - Lead the design and implementation of end-to-end data workflows, covering the reliable distribution of curated data to downstream systems including SQL Server, DynamoDB, S3, and other enterprise data consumers.

- Apply database security best practices including encryption at rest and in transit, access control, and auditing - Develop IaC solutions using Terraform for Databricks workspaces, AWS RDS, Aurora, and supporting resources - Automate operational tasks: cluster lifecycle, database provisioning, job scheduling, and backup management across cloud and on-premises environments

- Knowledge of data governance and compliance best practices. - Deep expertise in Databricks platform administration: Unity Catalog, workspace security, cluster management, and Auto Loader on AWS - Strong relational database engineering skills: schema design, query optimization, indexing, stored procedures, replication, and failover across SQL Server, Aurora, PostgreSQL, and/or MySQL

Databricks Platform & Database Engineer<br> Job Description

- Implement and enforce cluster policies, SQL warehouse governance, and workspace hardening. - Manage Databricks audit logs, lineage, and compliance reporting - Support SLA-driven 24/7 production environments; lead root cause analysis, troubleshoot incidents, and author operational runbooks

- Familiarity with large language models (LLMs), prompt engineering, or agent-based architectures. - 5+ years of experience in database engineering or data platform roles, across both relational/non-relational database administration and Databricks platform ownership - Proven, hands-on experience administering enterprise Databricks environments

- 5+ years of experience in database engineering or data platform roles, across both relational/non-relational database administration and Databricks platform ownership - Proven, hands-on experience administering enterprise Databricks environments - Collaborate with cross-functional teams: Work closely with business analysts, data scientists, DBAs, and DevOps engineers to ensure successful data platform implementations.

- Familiarity with ML tooling: MLflow, TensorFlow, PyTorch, or Scikit-learn - Understanding of Lakehouse AI architecture and how ML pipelines integrate with Unity Catalog and Delta Lake We welcome all qualified candidates who are currently eligible to work full-time in the United States to apply. However, please note that CoStar Group is not able to provide visa sponsorship for this position.

- Working knowledge of Data Visualization tools like Looker and PowerBI - Working knowledge of non-relational databases: DynamoDB, MongoDB, or equivalent NoSQL platforms - Familiarity with AI governance frameworks, model lineage, auditability, and compliance

- requirements for ML systems - Experience with kafka or other real-time streaming platforms, Postgres/RDBMS - Experience with Snowflake Administration

Preferred Qualifications - Working knowledge of Data Visualization tools like Looker and PowerBI - Working knowledge of non-relational databases: DynamoDB, MongoDB, or equivalent NoSQL platforms

- Experience implementing access controls, data privacy protections, and monitoring for AI workloads. - Familiarity with ML tooling: MLflow, TensorFlow, PyTorch, or Scikit-learn - Understanding of Lakehouse AI architecture and how ML pipelines integrate with Unity Catalog and Delta Lake

- Experience with kafka or other real-time streaming platforms, Postgres/RDBMS - Experience with Snowflake Administration - Experience working with and managing large consumer datasets to derive insights

- Advanced SQL and proficiency in PySpark and Python - Infrastructure-as-Code experience with Terraform; CI/CD pipeline management - Experience with cost optimization initiatives.