Senior Data Engineer

• Design, develop, and maintain scalable data pipelines and data products using Databricks and PySpark.
• Build and optimize ETL/ELT solutions supporting batch and near real-time data processing.
• Design, develop, and maintain enterprise Data Warehouse and Lakehouse solutions that support reporting, analytics, and AI/ML use cases.

• Develop and maintain enterprise data models using Delta Lake and Delta Tables.
• Design and implement dimensional data models, including Fact and Dimension tables, Star Schema, Snowflake Schema, and Slowly Changing Dimensions (SCD).

• Ensure data quality, reliability, scalability, and performance across the data platform.
• Implement best practices for code management, testing, deployment, and operational monitoring.

Databricks Platform & Governance

• Implement and manage Unity Catalog for centralized governance, data discovery, and security.
• Design and maintain governance frameworks utilizing:
• Role-Based Access Control (RBAC)
• Attribute-Based Access Control (ABAC)
• Fine-grained data permissions
• Data lineage and auditing
• Configure and manage Delta Sharing to support secure external and internal data collaboration.
• Support adoption and administration of Databricks Genie, including:
• Security and access governance controls

Performance Optimization

• Perform advanced PySpark performance tuning and troubleshooting.
• Optimize query performance, cluster utilization, partitioning strategies, and workload management.
• Identify bottlenecks and proactively improve platform efficiency and cost optimization.
• Optimize Data Warehouse and Lakehouse workloads to support high-performance reporting and analytical processing.

DevOps & Automation

• Design and implement CI/CD pipelines for Databricks solutions.
• Integrate Databricks development lifecycle with GitHub, GitHub Actions, and enterprise DevOps processes.
• Automate deployment, testing, code validation, and release management processes.
• Establish infrastructure and data engineering best practices.

Stakeholder Management

• Engage with business users, data consumers, architects, analysts, and technology leadership to gather requirements and deliver data solutions.
• Translate business requirements into scalable technical designs, data models, and platform capabilities.
• Communicate effectively with stakeholders across multiple organizational levels.
• Work independently while managing priorities and ensuring timely delivery of commitments.
• Provide technical guidance and mentorship to junior team members where required.

Production Support

• Participate in a rotating production support roster.
• Troubleshoot production incidents and prioritize issue resolution within established SLA requirements.
• Conduct root cause analysis and implement preventive measures.
• Ensure platform stability, reliability, and operational excellence.

Required Qualifications

Technical Skills

• Strong experience in:
• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
• 5-8 years of experience in Data Engineering, Data Warehousing, or Big Data technologies.
• Minimum 4+ years of hands-on Databricks experience in enterprise environments.
• Experience designing and implementing enterprise Data Warehouse solutions and modern Lakehouse.
• PySpark development and optimization.
• Delta Lake and Delta Tables.
• Unity Catalog.
• Databricks Workflows.
• RBAC and ABAC implementation within Unity Catalog.
• GitHub and Git-based development workflows.
• CI/CD implementation using GitHub Actions or equivalent.
• SQL and advanced query optimization.
• Cloud platforms (Azure, AWS, or GCP).
• Data security, governance, and compliance frameworks.
• Enterprise Data Warehouse architecture and implementation.
• Dimensional data modeling (Star Schema and Snowflake Schema).
• Fact and Dimension modeling.
• Slowly Changing Dimensions (SCD Type 1 & Type 2).
• Data Warehouse performance tuning and optimization.

Additional Technical Knowledge

• Data warehouse concepts and methodologies.
• Lakehouse architecture and Medallion design patterns.
• Data observability and monitoring.
• Infrastructure-as-Code (Terraform preferred).

Soft Skills

• Strong ownership mindset with high accountability and commitment to delivery.
• Ability to work independently with minimal supervision.
• Excellent analytical and problem-solving skills.
• Strong communication and stakeholder management capabilities.
• Ability to work effectively with stakeholders across business and technical functions.
• Ability to manage multiple priorities in a fast-paced environment.
• Collaborative team player with a proactive and customer-focused attitude.
• Willingness to participate in production support and on-call rotation schedules.

Preferred Qualifications

• Databricks Certified Data Engineer Associate or Professional certification.
• Experience implementing enterprise data governance frameworks.

Success Factors

• Experience supporting large-scale Lakehouse and Data Warehouse architectures.
• Experience working with regulated industries and compliance requirements.
• Knowledge of Data Mesh and modern data platform architectures.
• Experience integrating Databricks with Power BI, Tableau, or other BI and analytics platforms.

• The successful candidate will:
• Be the go-to Databricks engineering expert within the team.
• Drive governance and security best practices through Unity Catalog.
• Deliver reliable, scalable, and high-performing data solutions and enterprise data warehouse platforms.
• Partner effectively with business and technical stakeholders.
• Demonstrate strong ownership, accountability, and operational excellence.
• Contribute to continuous improvement of the organization's modern data platform capabilities.