Databricks Engineer

Our Business Intelligence group is a center of excellence at the Firm for data analytics, data management and data governance, with a focus on enhancing data and analytics literacy, reliability and accessibility, and developing an umbrella strategy for data and information governance throughout the Firm. The group is responsible for transforming data into actionable insights to support strategic decision-making and enhance operational efficiency. Our teams collect, analyze and interpret growing amounts of data from core internal and external sources. We develop and maintain datasets, dashboards and reports that provide real-time visibility into key performance indicators and trends. By leveraging advanced analytics and data visualization techniques, the Business Intelligence group identifies opportunities for growth, uncovers inefficiencies and helps predict future trends. We collaborate with all functions to ensure data integrity and to tailor insights to meet specific business needs. Through our expertise in data analysis and strategic thinking, the Business Intelligence team empowers the Firm to make informed, data-driven decisions that drive success and competitive advantage., The Databricks Engineer will join an agile delivery team responsible for building and supporting the Firm’s enterprise data platform on Azure Databricks. The role will design, develop, test, and operate scalable data pipelines and curated data products that integrate information from on-premises, cloud, and third-party systems. Working closely with business analysts, architects, application teams, and stakeholders, the specialist will translate business requirements into reliable technical solutions and deliver work through Azure DevOps and Microsoft Github. Strong communication skills are essential for explaining data designs, pipeline behavior, data quality, and delivery decisions to both technical and business audiences., * Design, build, test, deploy, and support scalable data pipelines and data products on Azure Databricks.

• Develop ingestion and transformation processes for data from on-premises, cloud, SaaS, and third-party sources using batch and, where appropriate, streaming patterns.
• Use SQL and Python or PySpark to transform, validate, and optimize large and complex datasets.
• Design and maintain reusable data structures across raw, refined, and curated layers, applying dimensional modeling and star schema principles where appropriate.
• Build reliable Delta Lake solutions and apply Databricks capabilities for orchestration, performance, observability, and data quality.·
• Manage and govern data assets through Unity Catalog, including appropriate access controls, lineage, documentation, and secure handling of sensitive information.
• Apply software engineering practices such as source control, code review, automated testing, reusable components, and CI/CD, and help develop coding standards, patterns, and templates that support consistent delivery across the team.
• Monitor production workloads, troubleshoot failures and performance issues, and improve the reliability, efficiency, and cost effectiveness of the platform.
• Partner with business analysts, architects, application teams, and subject-matter experts to define requirements, source data, technical designs, and delivery plans.
• Translate business concepts into trusted, well-documented data products that support analytics, reporting, and AI use cases.· Share knowledge and help plan and lead technical sessions that strengthen the team’s Databricks and data engineering capabilities.
• Contribute to data governance, architecture standards, and engineering practices that improve the quality, consistency, security, and availability of enterprise data.
• Stay current on Azure Databricks and related Azure capabilities and recommend practical improvements to the platform and delivery approach.

• At least five years of experience delivering production data engineering, data warehouse, or business intelligence solutions.
• Bachelor’s degree in technology, engineering, computer science, or a related field, or an equivalent combination of education and experience.
• Hands-on experience with Azure Databricks, Apache Spark, Delta Lake, and lakehouse architecture.
• Strong SQL skills and practical experience with Python or PySpark for data transformation and pipeline development.
• Experience designing and supporting batch data ingestion, ELT processes, orchestration, error handling, monitoring, and data quality controls.
• Strong knowledge of data architecture, dimensional modeling, star schemas, query design, and performance optimization.
• Experience integrating relational databases, APIs, files, cloud applications, and other enterprise data sources.
• Working knowledge of Unity Catalog, data governance, access controls, lineage, and secure data handling.
• Exposure to semantic models, business ontologies, agentic AI patterns, or standards such as Model Context Protocol (MCP).
• Experience with Git-based development, automated testing, and CI/CD practices using GitHub, GitHub Actions, Azure DevOps, or comparable tooling.
• Familiarity with relevant Azure services such as Azure Data Lake Storage, Azure Data Factory, Key Vault, and networking or identity services.
• Knowledge of software development life cycle concepts and agile delivery methods.
• Strong analytical, troubleshooting, and communication skills, with the ability to explain technical concepts clearly to business and technical stakeholders.
• Ability to work independently and collaboratively, manage competing priorities, and deliver reliable solutions within established timelines.