Hyderabad, Telangana, India
Information Technology
Full-Time
UST
Overview
Role Description
Key Accountabilities / Responsibilities:
Pyspark, SQL, Azure Databricks, AWS
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.
- 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
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