Primary Responsibilities:

Design, develop, and implement scalable data pipelines using Azure Databricks
Develop PySpark-based data transformations and integrate structured and unstructured data from various sources
Optimize Databricks clusters for performance, scalability, and cost-efficiency within the Azure ecosystem
Monitor, troubleshoot, and resolve performance bottlenecks in Databricks workloads
Manage orchestration and scheduling of end to end data pipeline using tool like Apache airflow, ADF scheduling, logic apps
Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Qualifications - External

Required Qualifications:

Undergraduate degree or equivalent experience
7+ years of experience in IT industry and 6+ years of experience in data engineering with 3+ years of hands-on experience in Azure Databricks
Proficiency in PySpark, Python and SQL for data processing in Databricks
Proven solid expertise in the Azure ecosystem, including Azure Synapse, Azure SQL, ADLS, and Azure Functions

Data Engineering Consultant