Senior Databricks Engineer

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols.
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets.
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.).
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information.

Role and Responsibilities:

• Architect, build, and maintain scalable ETL/ELT pipelines on the Databricks Lakehouse Platform using PySpark, Spark SQL, and Delta Lake.
• Design and implement medallion (bronze/silver/gold) data architectures and enforce data quality, governance, and lineage standards.
• Optimize Spark jobs and cluster configurations for performance and cost, including partitioning, caching, and autoscaling strategies.
• Implement and manage Unity Catalog for access control, data governance, and cross-workspace asset sharing.
• Build and orchestrate workflows using Databricks Workflows, Delta Live Tables, and CI/CD pipelines.
• Collaborate with data scientists, analysts, and business stakeholders to translate requirements into reliable data products.
• Establish engineering best practices, conduct code reviews, and mentor junior data engineers.
• Monitor production pipelines, troubleshoot failures, and drive root-cause analysis and continuous improvement.

Required Qualifications:

• 5+ years of data engineering experience, with 3+ years building production solutions on Databricks and Apache Spark.
• Expert proficiency in Python (PySpark) and advanced SQL.
• Deep hands-on experience with Delta Lake, Unity Catalog, and the medallion architecture pattern.
• Strong experience with at least one major cloud platform (AWS, Azure, or GCP) and its core data services.
• Proven track record optimizing Spark performance and managing cluster cost.
• Experience with data modeling, warehousing concepts, and building dimensional/analytics-ready datasets.
• Proficiency with Git-based version control, CI/CD, and infrastructure-as-code.
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

Preferred Qualifications:

• Databricks certification (Data Engineer Associate/Professional).
• Experience with Delta Live Tables, structured streaming, and real-time data processing.
• Familiarity with MLflow and supporting machine learning workflows in production.
• Experience with orchestration tools (Airflow, dbt) and data observability platforms.
• Exposure to data governance, security, and compliance frameworks (e.g., GDPR, HIPAA, SOC 2).
• Hands-on experience using AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate development, refactoring, and code review.
• Familiarity with large language model APIs and SDKs (e.g., Anthropic Claude, OpenAI) and prompt engineering for data and analytics use cases.
• Experience integrating GenAI capabilities into data pipelines or applications, including retrieval-augmented generation (RAG) and vector search.
• Awareness of responsible AI practices, including evaluation, guardrails, and cost/latency trade-offs when deploying LLM-based solutions.