Databricks Data Architect

The Databricks Data Architect is a senior technical lead responsible for building and optimizing a robust data platform in a financial services environment. You will lead a team of 10+ data engineers and own the end-to-end architecture and implementation of the Databricks Lakehouse platform. You will collaborate closely with application development and analytics teams to design scalable data solutions that drive business insights. This position demands deep expertise in Databricks (Azure), hands-on experience with PySpark and Delta Lake, and strong leadership to ensure best practices in data engineering, performance tuning, and governance.

Key Must have skills

• Lead Data Engineering Team: Lead, mentor, and manage a team of 10+ data engineers, providing technical guidance, code reviews, and career development to foster a high-performing team.
• Databricks Platform Ownership: Own the Databricks platform architecture and implementation, ensuring the environment is secure, scalable, and optimized for the organization’s data processing needs. Design and oversee the Lakehouse architecture leveraging Delta Lake and Apache Spark.
• Unity Catalog Implementation: Implement and manage Databricks Unity Catalog for unified data governance. Ensure fine-grained access controls and data lineage tracking are in place to secure sensitive financial data and comply with industry regulations.
• Cluster Provisioning & Policies: Provision and administer Databricks clusters (in Azure), including configuring cluster sizes, auto-scaling, and auto-termination settings. Set up and enforce cluster policies to standardize configurations, optimize resource usage, and control costs across different teams and projects.
• Databricks SQL Optimization: Collaborate with analytics teams to develop and optimize Databricks SQL queries and dashboards. Tune SQL workloads and caching strategies for faster performance and ensure efficient use of the query engine.
• Performance Tuning: Lead performance tuning initiatives for Spark jobs and ETL pipelines. Profile data processing code (PySpark/Scala) to identify bottlenecks and refactor for improved throughput and lower latency. Implement best practices for incremental data processing with Delta Lake, and ensure compute cost efficiency (e.g., by optimizing cluster utilization and job scheduling).
• Data Solutions Collaboration: Work closely with application developers, data analysts, and data scientists to understand requirements and translate them into robust data pipelines and solutions. Ensure that data architectures support analytics, reporting, and machine learning use cases effectively.
• DevOps Integration: Integrate Databricks workflows into the CI/CD pipeline using Azure DevOps and Git. Develop automated deployment processes for notebooks, jobs, and clusters (infrastructure-as-code) to promote consistent releases. Manage source control for Databricks code (using Git integration) and collaborate with DevOps engineers to implement continuous integration and delivery for data projects.
• Data Governance & Security: Collaborate with security and compliance teams to uphold data governance standards. Implement data masking, encryption, and audit logging as needed, leveraging Unity Catalog and Azure security features to protect sensitive financial data.
• Platform Innovation: Stay up-to-date with the latest Databricks features and industry best practices. Proactively recommend and implement improvements (such as new performance optimization techniques or cost-saving configurations) to continuously enhance the platform’s reliability and efficiency.

Minimum Qualifications

• Education & Experience: Bachelor’s degree in Computer Science, Information Systems, or a related field. 7+ years of experience in data engineering, data architecture, or related roles, with a track record of designing and deploying data pipelines and platforms at scale.
• Databricks & Spark Expertise: Significant hands-on experience with Databricks (preferably Azure Databricks) and the Apache Spark ecosystem. Proficient in building data pipelines using PySpark/Scala and managing data in Delta Lake format.
• Cloud Platform: Strong experience working with cloud data platforms (Azure preferred, or AWS/GCP). Familiarity with Azure data services (such as Azure Data Lake Storage, Azure Blob Storage, etc.) and managing resources in an Azure environment.
• SQL and Data Warehousing: Advanced SQL skills with the ability to write and optimize complex queries. Solid understanding of data warehousing concepts and performance tuning for SQL engines.
• Job Optimization & Performance: Proven ability to optimize ETL jobs and Spark processes for performance and cost efficiency. Experience tuning cluster configurations, parallelism, and caching to improve job runtimes and resource utilization.
• Unity Catalog & Security: Demonstrated experience implementing data security and governance measures. Comfortable configuring Unity Catalog or similar data catalog tools to manage schemas, tables, and fine-grained access controls. Able to ensure compliance with data security standards and manage user/group access to data assets.
• Leadership Skills: Experience leading and mentoring engineering teams. Excellent project leadership abilities to coordinate multiple projects and priorities. Strong communication skills to effectively collaborate with cross-functional teams and present architectural plans or results to stakeholders.
• Problem-Solving: Excellent analytical and problem-solving skills, with the ability to troubleshoot complex data pipeline issues quickly. Attention to detail in maintaining data quality and reliability across the platform.

Preferred Nice to have

• Certification: Databricks Certified Data Engineer Professional (highly preferred) or Databricks Certified Data Engineer Associate. Equivalent certifications in cloud data engineering or architecture (e.g., Azure Data Engineer, Azure Solutions Architect) are a plus.
• Financial Services Experience: Prior experience in the financial services industry or other highly regulated industries. Familiarity with financial data types, privacy regulations, and compliance requirements (e.g. handling PII, PCI data) can be beneficial.
• Advanced Education: Master’s degree in Computer Science, Data Engineering, or related field is a plus.
• Additional Big Data Technologies: Exposure to related big data and streaming tools such as Apache Kafka/Event Hubs, Apache Airflow or Azure Data Factory for orchestration, and BI/analytics tools (e.g., Power BI) is advantageous.
• CI/CD & DevOps: Experience implementing CI/CD pipelines for data projects. Familiarity with Databricks Repos, Jenkins, or other CI tools for automated testing and deployment of data pipelines.
• Performance and Cost Management: Proven strategies or projects where you significantly improved performance or reduced cloud costs (e.g., using spot instances, job scheduling, or improved query design) in a Databricks/Spark environment.

Tools & Technologies

• Databricks Lakehouse Platform: Databricks Workspace, Apache Spark, Delta Lake, Databricks SQL, MLflow (for model tracking).
• Data Governance: Databricks Unity Catalog for data cataloging and access control; Azure Active Directory integration for identity management.
• Programming & Data Processing: PySpark and Python for building data pipelines and Spark Jobs; SQL for querying and analytics;
• Cloud Services (Azure-focused): Azure Databricks, Azure Data Lake Storage (ADLS Gen2), Azure Blob Storage, Azure Synapse or SQL Database, Azure Key Vault (for secrets).
• DevOps & CI/CD: Azure DevOps (Azure Pipelines) for build/release pipelines, Git for version control (GitHub or Azure Repos); experience with Terraform or ARM templates for infrastructure-as-code is a plus.
  • Other Tools: Project and workflow management tools (JIRA or Azure Boards), monitoring tools (Azure Log Analytics, Spark UI or Databricks performance monitoring), and collaboration tools for documentation and design (Figma, Visio, Lucidcharts etc.) with strong leadership and collaboration skills to drive innovative data solutions.