DataBricks Data Engineer

Overview:

Prodapt is the largest specialized player in the Connectedness industry. As an AI-first strategic technology partner, Prodapt provides consulting, business reengineering, and managed services for the largest telecom and tech enterprises building networks and digital experiences of tomorrow. A ServiceNow-invested company, Prodapt has been recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider. A “Great Place To Work® Certified” company, Prodapt employs over 5,000 technology and domain experts across the Americas, Europe, India, Africa, & Japan. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 32,000 people across 80+ locations globally.

 

We are seeking an experienced Azure Databricks Engineer to join our team in Dallas, TX. The ideal candidate has strong hands-on experience in designing and building scalable data engineering solutions using Azure Databricks, PySpark, Python, SQL, and Azure Data Services including leading the modernization and migration of existing Python object-oriented applications into scalable PySpark and Spark SQL data-processing solutions on Azure Databricks.

Responsibilities:

Design, develop, and implement scalable data pipelines using Azure Databricks.
Build and optimize ETL/ELT pipelines using PySpark, Python, and SQL.
Develop data solutions using the Medallion Architecture (Bronze, Silver, and Gold layers).
Work with Delta Lake, Delta tables, and advanced Databricks optimization techniques.
Integrate Databricks with Azure services such as ADLS Gen2, Azure Data Factory, Azure Synapse, and Azure Key Vault.
Develop and manage Databricks Workflows, Jobs, and Notebooks.
Implement data quality, monitoring, error handling, and performance optimization.
Collaborate with Data Architects, Data Engineers, Data Scientists, and business stakeholders.
Establish best practices for CI/CD, Git/version control, code reviews, and automated deployments.
Lead technical discussions and provide guidance to junior engineers.
Analyze existing Python OOP applications and redesign single-node processing logic for distributed Spark execution.
Design, develop, and deploy enterprise-scale data pipelines on Azure Databricks; build reusable PySpark frameworks and utility modules.
Implement Delta Lake solutions using the Bronze, Silver, Gold architecture.
Build robust ETL/ELT pipelines with Azure Data Factory, ADLS Gen2, and Azure Synapse Analytics.
Implement data quality, reconciliation, validation, and monitoring frameworks.
Optimize Spark jobs using partitioning, bucketing, caching, broadcast joins, Adaptive Query Execution, and Delta optimization.

Requirements:

10+ years of overall Data Engineering experience, including designing and implementing ETL/ELT pipelines with Azure Data Factory and other Azure services.

Bachelor’s degree in Computer Science, Engineering, or a related field; OR equivalent combination of education and relevant experience.
Strong hands-on experience with Azure Databricks; 4+ years of experience across Azure services and Databricks (ADLS, ADF, Azure DevOps, etc.).
7+ years of Python development experience, with the ability to design and build reusable libraries.
4+ years of experience with Snowflake or SQL (No-SQL experience is a plus).
Expert-level knowledge of PySpark and Spark SQL.
Strong programming experience in Python and SQL.
Experience with Delta Lake and Lakehouse architecture.
Strong experience with Azure Data Lake Storage (ADLS Gen2).
Experience with Azure Data Factory (ADF) and other Azure data services.
Strong understanding of ETL/ELT, data modeling, and large-scale data pipelines.
Experience with performance tuning and optimization in Databricks/Spark.
Experience with Git, CI/CD, and DevOps practices.

 

Preferred Skills
Databricks certifications.
Experience with Unity Catalog and Databricks governance/security.
Experience with Terraform or Infrastructure as Code.
Knowledge of Azure DevOps.
Experience designing APIs and integrating with React JS within a cloud platform