Job Description :

Mandates : Azure Data Bricks + PySpark + SQL (All 3 skills are mandate)

Tech Stack : Databricks, PySpark, Python, SQL and NoSQL databases, Scala, Spark-SQL

Experience with Azure: ADLS, Databricks, Stream Analytics, SQL DW, COSMOS DB, Analysis Services, Azure Functions, Serverless Architecture, ARM Templates

Skills :

- Working knowledge of Azure Analytics features such as Stream Analytics, Machine Learning and Application Insights.

- Modelling and ETL knowledge with Erwin, Azure Databricks, Data Factory, SSIS and Azure Synapse.

- Develop and maintain system integrations using, Azure Logic Apps, Integration Services, Power Automate, PowerApps, etc.

- Microsoft Azure data platform experience with Power BI, Azure Data Lake, data warehouse and Data Factory.

- Knowledge on migrating on-premises applications to Azure PaaS and Azure LaaS.

- Attention to detail and ability to coordinate multiple tasks, set priorities and meet deadlines

- Should be well versed with Data Structures & algorithms

- Excellent analytical and problem-solving skills.

- Ability to work independently as a self-starter, and within a team environment.

- Good Communication skills- Written and Verbal

- Work with business to understand their current state architecture and contributing data sources, technologies, interfaces, performance issues and system configurations.

- Work closely with the team across the overall data warehousing program including data acquisition, data curation and data syndication.

- Apply knowledge of the Microsoft platform tooling involved in successful Azure implementations.

- Serve as the subject matter expert with respect to Azure, cloud applications and system administration best practices.

- Experience designing data lakes, database schemas and data models for large scale data platform implementation on Azure.

- Develop system integrations using, Azure Logic Apps, Integration services, Power Automate, PowerApps, etc.

- Perform project management activities including project documentation, business requirements and project tracking.

- Ensuring data quality and consistency through data cleaning, transformation, and integration processes.

- Monitoring and troubleshooting data-related issues within the Azure environment to maintain high availability and performance.

- Collaborating with data scientists, business analysts, and other stakeholders to understand data requirements and implement appropriate data solutions.

- Implementing data security measures, including encryption, access controls, and auditing, to protect sensitive information.

- Automating data pipelines and workflows to streamline data ingestion, processing, and distribution tasks.

- Utilizing Azure's analytics services, such as Azure Synapse Analytics, to provide insights and support data-driven decision-making.

- Keeping abreast of the latest Azure features and technologies to enhance data engineering processes and capabilities.

- Documenting data procedures, systems, and architectures to maintain clarity and ensure compliance with regulatory standards.

- Providing guidance and support for data governance, including metadata management, data lineage, and data cataloguing.