Senior Data Engineer

3+ years of experience in data engineering, with a strong background in building and scaling data-intensive applications. Solid understanding of data warehousing concepts, dimensional modeling, and ETL best practices. Proficiency in SQL and at least one programming language such as Python or Java. Strong hands-on experience with Snowflake, including building and optimizing data pipelines, data models, and transformations. Experience working with one or more major cloud platforms (e.g., AWS, Azure, or GCP). AWS preferred. Experience with workflow orchestration tools such as Apache Airflow. Familiarity with data transformation tools and frameworks such as dbt (Data Build Tool), PySpark, or Snowpark. Experience working with data visualization tools like Tableau or Looker. Working knowledge of Git and CI/CD best practices. A collaborative mindset and the ability to work effectively with both technical and non-technical team members.

Design and implement data pipelines and ETL processes to support large-scale data products. Develop and deliver efficient data models and products that align with business needs. Gather and understand requirements, then design data models and solutions tailored to analytical and usage demands. Apply data quality, governance, and best practices to ensure consistency, accuracy, and reliability across all data products; establish monitoring systems to maintain partner trust throughout the data lifecycle. Perform root cause analysis, implement preventative solutions, and monitor data processes to ensure quality and performance, including using cost analysis to design efficient, cost-effective solutions. Collaborate closely with stakeholders to understand requirements, translate them into detailed technical solutions, and plan for incremental, on-time delivery. Mentor junior data engineers through code reviews, sharing best practices, and helping to foster a culture of high performance and engineering excellence.