Senior DevOps Platform Engineer

Key Accountabilities / Responsibilities:

• Provide technical direction and leadership to data engineers on data platform initiatives - ensuring adherence to best practices in data modelling, End to End pipeline design, and code quality.
• Review and optimize PySpark, SQL, and Databricks code for performance, scalability, and maintainability.
• Offer engineering support and mentorship to data engineering teams within delivery squads, guiding them in building robust, reusable, and secure data solutions.
• Collaborate with architects to define data ingestion, transformation, and storage strategies leveraging Azure services such as Azure Data Factory, Azure Databricks, Azure Data Lakes
• Drive automation and CI/CD practices in data pipelines using tools such as Git, Azure DevOps, and DBT (good to have).
• Ensure optimal data quality and lineage by implementing proper testing, validation, and monitoring mechanisms within data pipelines.
• Stay current with evolving data technologies, tools, and best practices, continuously improving standards, frameworks, and engineering methodologies.
• Troubleshoot complex data issues, analyse system performance, and provide solutions to development and service challenges.
• Coach, mentor, and support team members through knowledge sharing sessions, technical reviews.

Required Skills & Experience:

* Developer / engineering background of large-scale distributed data processing systems (or experience in equal measure). Can provide constructive feedback based on knowledge.

* Proficient in designing scalable and efficient data models tailored for analytical and operational workloads, ensuring data integrity and optimal query performance.

* Practical experience implementing and managing Unity Catalog for centralized governance of data assets across Databricks workspaces, including access control, lineage tracking, and auditing.

* Demonstrated ability to optimize data pipelines and queries using techniques such as partitioning, caching, indexing, and adaptive execution strategies to improve performance and reduce costs.

* Programming in Pyspark (Must), SQL(Must), Python (Good to have).

* Experience with Databricks (Mandatory) and DBT (Good to have) is required

* Implemented cloud data technologies on either Azure (Must) other optional GCP, Azure or AWS.

* Knowledge around shortening development lead time and improving data development lifecycle

* Worked in an Agile delivery framework.

Pyspark, SQL, Azure Databricks, AWS