Senior Data Engineer

Summary

Join Kaseya, a leading provider of IT infrastructure and security management solutions, as a Data Engineer. Reporting to the Data Engineering Manager, you will design, build, and maintain scalable data pipelines. Responsibilities include developing ETL pipelines, implementing data lake and warehouse solutions, optimizing query performance, and ensuring data governance and security. You will collaborate with analytics teams and evaluate emerging technologies. This role requires a Bachelor's degree in a related field, 2+ years of experience in data engineering, proficiency in SQL and a programming language, and experience with data integration/ETL tools and cloud data warehouses. Kaseya offers a fast-paced, high-growth environment.

Requirements

• Bachelor’s degree (or equivalent) in Computer Science, Engineering, or related field
• 2+ years of experience in data engineering or related role
• Proficient in SQL and at least one programming language (Python, Scala, or Java)
• Hands-on experience with data integration/ETL tools (e.g., Matillion, Talend, Airflow)
• Familiarity with modern cloud data warehouses (Snowflake, Redshift, or BigQuery)
• Strong problem-solving skills and attention to detail
• Excellent communication and team collaboration skills
• Ability to work in a fast-paced, high-growth environment

Responsibilities

• Design and Develop ETL Pipelines : Create high-performance data ingestion and transformation processes, leveraging tools like Matillion, Airflow, or similar
• Implement Data Lake and Warehouse Solutions : Develop and optimize data warehouses/lakes (Snowflake, Redshift, BigQuery, or Databricks), ensuring best-in-class performance
• Optimize Query Performance : Continuously refine queries and storage strategies to support large volumes of data and multiple use cases
• Ensure Data Governance & Security : Collaborate with the Data Governance team to ensure compliance with privacy regulations and corporate data policies
• Troubleshoot Complex Data Issues : Investigate and resolve bottlenecks, data quality problems, and system performance challenges
• Document Processes & Standards : Maintain clear documentation on data pipelines, schemas, and operational processes to facilitate knowledge sharing
• Collaborate with Analytics Teams : Work with BI, Data Science, and Business Analyst teams to deliver timely, reliable, and enriched datasets for reporting and advanced analytics
• Evaluate Emerging Technologies : Stay informed about the latest tools, frameworks, and methodologies, recommending improvements where applicable