Lead II - Data Engineering- Pyspark, Sql, Azure databricks, ELT

7 - 14 Years
1 Opening
Trivandrum

Role Proficiency:

This role requires proficiency in developing data pipelines including coding and testing for ingesting wrangling transforming and joining data from various sources. The ideal candidate should be adept in ETL tools like Informatica Glue Databricks and DataProc with strong coding skills in Python PySpark and SQL. This position demands independence and proficiency across various data domains. Expertise in data warehousing solutions such as Snowflake BigQuery Lakehouse and Delta Lake is essential including the ability to calculate processing costs and address performance issues. A solid understanding of DevOps and infrastructure needs is also required.

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Additional Comments:

We are seeking an experienced and driven Senior Data Engineer with 8+ years of hands-on experience in designing and building robust, scalable data pipelines. This role requires deep expertise in PySpark, SQL, and cloud data platforms such as Azure Databricks, with additional exposure to AWS or GCP environments. The ideal candidate will also be well-versed in dimensional modeling techniques (e.g., Kimball/star schema) and best practices for ETL/ELT pipeline development. You will collaborate with data architects, analysts, and business stakeholders to deliver high-quality data solutions that drive insights and business value. Key Responsibilities: Design, build, and optimize scalable ETL/ELT pipelines using PySpark and SQL Develop data workflows on Azure Databricks and integrate across cloud platforms (AWS or GCP) Implement and maintain data models using Kimball/star schema or similar dimensional modeling approaches Ensure data quality, consistency, and performance across large datasets Collaborate with cross-functional teams to understand business requirements and translate them into scalable data solutions Contribute to data architecture and platform decisions in a cloud-native environment Participate in code reviews, documentation, and Agile team ceremonies Must-Have Skills: 8+ years of experience in Data Engineering or a related field Strong hands-on experience with PySpark for data transformation and processing Advanced SQL skills for querying and managing large-scale datasets Proven experience with Azure Databricks for big data development Familiarity with cloud platforms like AWS and/or GCP Solid understanding of dimensional data modeling using Kimball/star schema Experience building and maintaining ETL/ELT data pipelines in production environments Nice-to-Have Skills: Experience with orchestration tools (e.g., Apache Airflow, Azure Data Factory) Exposure to data governance, cataloging, and lineage tools Familiarity with Delta Lake and Lakehouse architectures Proficiency in Python beyond PySpark (e.g., for utilities, API integration) Background in business domains such as finance, e-commerce, or retail analytics

Pyspark,Sql,Azure databricks,ELT