Data Engineer - Snowflake

As a Data Engineer - Snowflake at our company, you will play a crucial role in the migration of Datastage jobs to Snowpark, ensuring accurate translation of existing logic. Your responsibilities will also include performing unit testing of the developed Snowpark code, utilizing Airflow for job orchestration, and conducting testing to compare outcomes with original Datastage jobs. In addition, documenting developed code and processes is essential, allowing for ease of understanding and future reference.

To apply for the Data Engineer - Snowflake position, you will need a Bachelor's degree in Computer Science, Engineering, or a similar field. Additionally, candidates should have at least 5 years of experience with Snowflake and 2 years of experience with Snowpark. Familiarity with Airflow, Snowflake on Azure, and at least one ETL/ELT tool, preferably Datastage, will significantly strengthen your application.

Important skills for a Data Engineer - Snowflake include a solid understanding of SQL and data warehousing concepts. Proficiency in scripting languages such as Python, strong problem-solving abilities, and excellent communication and collaboration skills are essential. The ability to work independently in a remote environment while effectively managing tasks within deadlines is also a critical aspect of success in this role.

Remote work for the Data Engineer - Snowflake role is centered around collaboration within the India Time Zone, allowing team members to communicate effectively. Our company fosters a strong remote work culture where you can thrive while working independently. Regular meetings, use of collaborative tools, and ongoing support help create an engaging work atmosphere, even from afar.

A Data Engineer - Snowflake should be familiar with a range of technologies, including Snowflake, Snowpark, and Airflow. Experience with Azure is a plus as well as familiarity with ETL/ELT tools, particularly Datastage. A solid understanding of SQL and data warehousing concepts, as well as scripting languages like Python, will significantly enhance your effectiveness in this role.

During the migration from Datastage to Snowpark, it’s crucial to first understand and document the existing logic of Datastage jobs. You'll then need to transform that logic into Snowpark code while ensuring all functionalities remain intact. Testing phases, including unit and parallel run testing, will validate that data quality is maintained, making problem-solving and analytical skills essential.

I handle unit testing by setting clear objectives and creating detailed test cases before development begins. Once code is developed, I use sample datasets to test functionality and ensure that outputs meet the expected results. Continuous integration tools can help automate some of these processes, which further enhances reliability.

Airflow plays a crucial role in orchestrating and scheduling my data workflows, ensuring tasks run in an efficient and timely manner. I typically set up directed acyclic graphs (DAGs) in Airflow that define the order of operations, allowing for dependencies between tasks to be managed seamlessly while improving overall performance.

I have extensive experience with SQL, using it for data retrieval, manipulation, and analysis in various projects. Additionally, my work in data warehousing has involved designing schemas, managing ETL processes, and ensuring data integrity across systems, which are all vital for a Data Engineer's responsibilities.

I approach collaboration with remote teams by utilizing digital communication tools and establishing regular check-ins. I make it a priority to cultivate strong relationships through clear communication and sharing progress updates. This way, everyone is aligned and can contribute effectively, regardless of location.

To ensure data quality during migrations, I implement thorough testing phases at multiple stages. This includes unit testing of the Snowpark code as well as parallel runs that compare outputs directly against the original Datastage jobs using production data. Documentation of processes also aids in maintaining data integrity.

My experience with Snowflake includes designing and implementing data architecture, performance tuning, and query optimization. I've successfully managed data storage and retrieval effectively while leveraging the powerful functionalities Snowflake offers, such as its scalable compute resources and advanced data sharing capabilities.

One challenging problem I solved involved optimizing the performance of slow-running queries in a data pipeline. By analyzing execution plans and adjusting indexes, I significantly reduced processing time, enhancing overall system efficiency. Collaborative discussions with team members also led to insights that aided in solving the issue.

I utilize project management tools to break down my tasks and set deadlines, helping me to prioritize effectively. Daily stand-ups allow me to recalibrate and adapt my focus, while also keeping all stakeholders informed. Having clear, actionable goals reduces ambiguities and fosters productivity.

I have experience with Python, primarily focusing on automation and integration tasks. This involves writing scripts to manipulate data, interact with APIs, and streamline processes within my data engineering work, enabling me to enhance efficiencies while ensuring high-quality deliverables.