Primary Responsibilities:
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
Preferred Qualifications:
Bachelor's degree in Computer Science, Engineering, Mathematics or related discipline
Extensive knowledge of data architecture principles (e.g., Data Lake, Databricks Delta Lake, Data Warehousing, etc.)
Extensive knowledge of data modelling techniques including slowly changing dimensions, aggregation, partitioning and indexing strategies
Experience working with LLMs
Ability to independently troubleshoot and performance tune large scale enterprise systems
Excellent collaborator with experience working effectively with cross-functional teams such as leadership, product management and engineering, with a willingness to inspire other data engineers, data scientists and analysts
Solid communication skills with the ability to communicate technical concepts to both technical and non-technical audiences
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