Tech Lead – Azure Databricks

• Develop, implement, and maintain the information data lake and insight platforms for organizational decision support
• Partner with business stakeholders and customers to understand data and reporting requirements
• Bring datasets together to answer business questions and drive growth
• Design ETL processes to ingest data into data warehouses, data lakes, and reporting applications
• Collaborate with business customers and development teams to define requirements and deliver flexible, scalable, end-to-end solutions
• Drive business intelligence solutions across requirements, data modelling, ETL, metadata, reporting, and dashboarding
• Design, create, manage, and support the business use of large datasets
• Define SLAs for areas of ownership
• Prepare process maps, workflows, business cases, and simple business models
• Analyze impacts of requested changes, enhancements, and defect fixes and identify dependencies
• Implement code or configure/customize products and provide design and architecture input
• Analyze frameworks and tools, review code, and provide improvement feedback
• Provide application- or function-level architectural designs and documentation
• Implement architectural patterns and communicate architecture direction to the business
• Identify causes of errors and potential solutions

• 8+ years of experience
• Experience with data modelling, SQL, ETL, data warehousing, and data lakes
• Experience writing SQL and Python scripts
• Experience building dashboards and reports using Microsoft Power BI
• Experience transforming data using Azure Data Factory
• Experience working with Spark and Azure Databricks
• Familiarity with Python, PySpark, or JavaScript
• Knowledge of Azure Databricks and MPP databases
• Experience with data lake development
• Exposure to NoSQL databases such as Cosmos DB and MongoDB
• Experience following Agile processes
• Experience with distributed version control systems such as Git
• Experience with Mulesoft, Kingswaysoft, or Marketo
• Working knowledge of requirement management and analysis processes, tools, and methodologies
• Working knowledge of technology product/platform standards and specifications
• Working knowledge of architecture industry tools and frameworks
• Working knowledge of architectural elements, SDLC, and methodologies
• Knowledge of statistical and machine learning techniques including classification, linear regression modelling, clustering, and decision trees
• Familiarity with mainstream commercial and open-source data science/analytics software tools

• Flexible work arrangements
• Inclusive team environment
• Career-oriented skilling models
• Continuous learning, unlearning, and relearning
• Agile self-determination, trust, transparency, and open collaboration
• Support needed for realization of business goals
• Stable employment
• Great atmosphere and ethical corporate culture