Senior Databricks Engineer
Indexed description
This role requires strong technical leadership, solution design capabilities, stakeholder management skills, and the ability to drive data transformation initiatives from requirements gathering through deployment and production support.
Technical Expertise
Databricks (PySpark, SQL, Notebooks, Workflows) , Apache Spark , Delta Lake , Python, Azure
Data Factory (ADF) ,Azure Synapse Analytics ,AWS S3, Glue, Lambda , Unity Catalog
MLflow ,Databricks Job Clusters ,CI/CD Pipelines ,GitLab / GitHub ,Data Governance & Data
Quality Frameworks, Power BI / Tableau , Data Cataloging ,Lakehouse Architecture ,Medallion
Architecture, Performance Tuning & Optimization
Key Responsibilities
- Lead the design, development, and implementation of enterprise-scale data engineering
- Own end-to-end project delivery, including requirement gathering, solution design,
- Collaborate directly with clients, business stakeholders, architects, and product owners to
- Conduct client discussions, solution workshops, effort estimations, technical presentations,
- Design and implement scalable Lakehouse architectures and data platforms across Azure and
- Lead the development of high-performance ETL/ELT pipelines supporting both batch and
- Drive best practices around coding standards, architecture governance, version control, CI/CD
- Architect and optimize Databricks Workflows, Job Clusters, Delta Tables, and Spark
- Implement data governance frameworks using Unity Catalog, ensuring proper access controls,
- Mentor and guide junior and mid-level data engineers through code reviews, technical
- Lead technical teams and coordinate project activities to ensure timely and successful project
- Collaborate with Data Scientists, BI Teams, and Analytics stakeholders to enable advanced
- Drive automation initiatives through Infrastructure as Code (IaC), DevOps practices, and
- Establish monitoring, observability, and performance tracking frameworks for data platforms
- Ensure security, compliance, data quality, and operational reliability across enterprise data
- Prepare and maintain technical architecture documents, design specifications, implementation
Requirements
- 8+ years of experience in Data Engineering, Big Data, and Analytics platforms.
- Minimum 5+ years of hands-on experience working with Databricks, Apache Spark, PySpark,
- Strong expertise in designing and implementing enterprise-scale Data Lake, Lakehouse, and
- Deep understanding of Spark internals, cluster management, performance tuning, partitioning
- Extensive experience working with Delta Lake, schema evolution, ACID transactions, and
- Hands-on experience integrating Databricks with Azure (ADF, Synapse) and/or AWS (S3,
- Proven experience handling end-to-end project delivery and managing technical engagements
- Experience leading development teams and mentoring engineers in enterprise environments.
- Strong understanding of data modeling, dimensional modeling, Medallion Architecture, and
- Expertise in implementing CI/CD pipelines, DevOps practices, and automated deployment
- Experience with Unity Catalog or equivalent governance platforms.
- Excellent communication, stakeholder management, presentation, and client-facing skills.
- Ability to lead technical discussions, architecture reviews, and solution design workshops.
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a
Information Security & ISO 27001 Compliance
Security is at the heart of everything we do. In this role, you will be strictly required to uphold our Information Security Management System (ISMS) policies in alignment with ISO/IEC 27001 standards. Responsibilities include safeguarding sensitive asset data, completing mandatory security awareness training, reporting potential security incidents or vulnerabilities immediately, and ensuring that daily operations comply with our rigorous data protection protocols.
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