Senior Azure Data Engineer
2 months ago
Position : Azure Data Engineer.
Experience : 6+ years.
Location Remote.
Skills: Snowflake, ADF, python and ETL, spark, scala, SQL.
- An offshore Data Engineer to work with the Investment Analytics team.
- This person needs to have a strong exp of Data warehousing and ETL leveraging Snowflake, ADF and Python.
- A strong candidate to work with the Data Science team.
- Collaborate with all the teams to drive discovery and requirements gathering for data management and business analytics efforts.
- Automate ETL for data feeds many sources, and maintain databases.
- Automate data validation of data feeds from many sources, to enhance data accuracy.
- Design and optimize processes for data intake, validation, mining, and engineering.
- Work on a Scrum team to deliver on projects and initiatives that impact business development, client support, business management, investment management, and all functional areas of the firm.
- Designing and developing data pipelines: You will design and implement scalable and efficient data pipelines using Azure Databricks, PySpark, and Scala.
- This includes data ingestion, data transformation, and data loading processes.
- Data modeling and database design: You will design and implement data models to support efficient data storage, retrieval, and analysis.
- This may involve working with relational databases, data lakes, or other storage solutions on the Azure platform.
- Data integration and orchestration: You will leverage Azure Data Factory (ADF) to orchestrate data integration workflows and manage data movement across various data sources and targets.
- This includes scheduling and monitoring data pipelines.
- Data quality and governance: You will implement data quality checks, validation rules, and data governance processes to ensure data accuracy, consistency, and compliance with relevant regulations and standards.
- Performance optimization: You will optimize data pipelines and queries to improve overall system performance and reduce processing time.
- This may involve tuning SQL queries, optimizing data transformation logic, and leveraging caching techniques.
- Monitoring and troubleshooting: You will monitor data pipelines, identify performance bottlenecks, and troubleshoot issues related to data ingestion, processing, and transformation.
- You will work closely with cross-functional teams to resolve data-related problems.
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