Key Responsibilities:
- Design, develop, and maintain robust and scalable data pipelines to support various data processing and analytics needs.
- Implement and optimize SQL queries to extract, transform, and load (ETL) data from various data sources into our data warehouse.
- Work with relational databases to ensure data consistency, integrity, and performance.
- Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet their needs.
- Develop and maintain data models and schemas to support business intelligence and reporting activities.
- Utilize distributed computing frameworks, such as Apache Spark, to process large datasets efficiently.
- Debug and troubleshoot data-related issues, ensuring data quality and reliability.
- Document data engineering processes, workflows, and best practices.
This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications
Bachelors degree, OR 3+ years of relevant work experience
Preferred Qualifications
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent practical experience).
Strong proficiency in SQL and experience working with relational databases (e.g., MySQL, PostgreSQL, SQL Server).
Solid understanding of distributed systems concepts, including parallel processing, data partitioning, and fault tolerance.
Hands-on experience with Python for data manipulation, scripting, and automation.
Practical experience with Apache Spark for distributed data processing.
Basic understanding of data warehousing concepts and ETL processes.
Excellent problem-solving skills and attention to detail.
Strong communication skills, with the ability to work effectively in a team-oriented environment.