Applications Consultant 2 - Senior Data Engineer

About the job you’re considering

We are seeking a highly skilled Senior Data Engineer to join our data engineering team. The ideal candidate will have extensive experience in building robust ETL pipelines, implementing efficient data ingestion strategies, and working with tools like Python, Spark, and Databricks. This role will involve collaborating with cross-functional teams to ensure data integrity, scalability, and optimal performance for our data-driven solutions.

Your role

• Design, develop, and maintain robust and scalable data pipelines using Azure Data Factory (ADF).
• Build and optimize data warehousing solutions on Snowflake, ensuring performance, scalability, and data quality.
• Leverage Databricks for data processing, transformation, and advanced analytics using Spark and Python/Scala.
• Implement and manage data orchestration and scheduling using Control-M.
• Collaborate with data scientists and analysts to understand their data requirements and provide efficient data solutions.
• Monitor and troubleshoot data pipelines and data warehouse performance, ensuring data integrity and reliability.
• Implement data quality checks and validation processes to ensure accuracy and consistency of data.
• Develop and maintain data models and schemas for optimal data storage and retrieval.
• Implement and maintain data security and governance policies.
• Stay up-to-date with the latest advancements in Azure cloud technologies and data engineering best practices.
• Document data pipelines, data models, and ETL processes.
• Participate in code reviews and contribute to the development of data engineering standards and best practices.
• Troubleshoot and resolve data-related issues in a timely manner.

Your skills and experience

• Azure Cloud: Deep understanding of Azure cloud services, particularly in the data and analytics domain.
• Azure Data Factory (ADF): Proven experience in designing, building, deploying, and managing complex data pipelines using ADF, including data flows and mapping data flows.
• Databricks: Hands-on experience with Databricks, including Spark programming (Python or Scala), Delta Lake, and optimizing Spark jobs for performance.
• Snowflake: Extensive experience in designing, developing, and administering data warehouses on Snowflake, including data modeling, SQL development, performance tuning, and security features.
• Control-M: Experience in using Control-M for workflow orchestration, scheduling, monitoring, and managing batch processes.
• SQL: Strong proficiency in SQL for data querying, manipulation, and analysis across different database systems.
• Data Modeling: Solid understanding of different data modeling techniques (e.g., relational, dimensional).
• ETL/ELT Concepts: Comprehensive understanding of ETL and ELT principles and best practices.
• Scripting: Proficiency in at least one scripting language such as Python for automation and data manipulation.
• Version Control: Experience with Git and related version control workflows.
• Data Quality: Understanding of data quality principles and experience implementing data quality checks and processes.