Description

Design and develop ETL/ELT solutions on Azure Databricks, LakeBase and Spark

Develop, implement, and deploy large scale data pipelines empowering machine learning algorithms, insights generation, business intelligence dashboards, reporting and new data products

Design, build, optimize, and manage modern large-scale data pipelines ETL/ELT processing to support data integration for analytics, machine learning features and predictive modelling

Consume data from a variety of sources (RDBMS, APIs, FTPs and other cloud storage) & formats (Excel, CSV, XML, JSON, Parquet, Unstructured)

Write advanced / complex SQL with performance tuning and optimization

Identify ways to improve data reliability, data integrity, system efficiency and quality

Participate in architectural evolution of data engineering patterns, frameworks, systems, and platforms including defining best practices and standards for managing data collections and integration

Mentor other data engineers and provide technical direction by teaching other data engineers how to leverage cloud data platforms

Required Qualifications:

7 + years of experience in data engineering, data integration, data modeling, data architecture, and ETL/ELT processes to provide quality data and analytics solutions

5 + years of experience in Python

2 + years of experience in Apache Spark (PySpark/Spark SQL)

2+ years building and deploying Cloud based solutions using - Azure Databricks with UC, Snowflake, Functions, Service Bus

2+ years of experience in SQL with designing complex data schemas and query performance optimization

Experience with DevOps automation with Terraform.

Experience with CI/CD process and tools – GitHub Actions, GIT, Artifactory, Sonar

Skill-1 (Data Engineering and ETL/ELT processes)

Key Skills

Education

Bachelor's degree