Data Engineer

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Responsibilities

• 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:
• Natural language data interaction capabilities
• 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

• 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 architectures.

Technical Skills

Strong experience in:

• PySpark development and optimization
• Delta Lake and Delta Tables
• Unity Catalog
• 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

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.
• 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

Success Factors

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.