Azure Databricks Lead Developer

Job Title: A zure Databricks Lead Developer .


Employment Type: Full-Time
Work Mode: Plano, TX

Required Qualifications:


Candidate must be located within commuting distance of Plano, TX or be willing to relocate to the area. This position may require travel within the US
Bachelor's degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education
At least 4 years of experience with Information Technology
Proven years of hands-on experience in Azure, Databricks
Proficiency in data engineering tools: Spark, Kafka, Airflow, SQL, Python
Strong understanding of retail or CPG data domains
Familiarity with vector search and semantic retrieval for retail use cases
Expertise in data privacy and compliance in consumer-facing environments
Good experience in end-to-end implementation of data warehouse and data marts
Strong understanding of Data Warehousing concepts of Data Vault, Dimensional modelling
Extensive experience with CI/CD platforms such as GitLab CI, GitHub Actions, Azure Pipelines, and Jenkins
Experience designing and implementing complex solutions for distributed systems
Experience in leading and mentoring teams


Preferred Qualifications:
Experience in Teradata
Proven hands-on experience in Data migration solutions and able to demonstrate the same
Experience in Relational Modeling, Dimensional Modeling and Modeling of Unstructured Data
Familiarity with log and monitoring solutions
Experience in utilizing development containers, unit testing/code quality review/linting - standard best practice
Good understanding of Agile software development frameworks
Strong communication and Analytical skills
Ability to work in teams in a diverse, multi-stakeholder environment comprising of Business and Technology teams
Experience and desire to work in a global delivery environment
Experience and detailed knowledge with Master Data Management, ETL, Data Quality, metadata management, data profiling, micro-batches, streaming data loads