Job
Description
Description - 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 Effective collaboration with Architecture team in designing solutions and with product owners with validating the implementations Implementing best practices to enable data quality, monitoring, logging and alerting the failure scenarios and exception handling Documenting step by step process to trouble shoot the potential issues and deliver cost optimized cloud solutions Provide technical leadership, mentorship, and best practices for junior data engineers Stay up to date with Azure and Databricks advancements to continuously improve data engineering capabilities 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 - Required Qualifications: B.Tech or equivalent 7+ years of overall experience in IT industry and 6+ years of experience in data engineering with 3+ years of hands-on experience in Azure Databricks Hands-on experience with Delta Lake, Lakehouse architecture, and data versioning Experience with CI/CD pipelines for data engineering solutions (Azure DevOps, Git) Solid knowledge of performance tuning, partitioning, caching, and cost optimization in Databricks Deep understanding of data warehousing, data modeling (Kimball/Inmon), and big data processing Solid expertise in the Azure ecosystem, including Azure Synapse, Azure SQL, ADLS, and Azure Functions Proficiency in PySpark, Python and SQL for data processing in Databricks Proven excellent written and verbal communication skills Proven excellent problem-solving skills and ability to work independently Proven ability to balance multiple and competing priorities and execute accordingly Proven highly self-motivated with excellent interpersonal and collaborative skills Proven ability to anticipate risks and obstacles and develop plans for mitigation Proven excellent documentation experience and skills Preferred Qualifications: Azure certifications DP-203, AZ-304 etc. Experience in infrastructure as code, scheduling as code, and automating operational activities using Terraform scripts
