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Medium — remote role, popular data engineering title, and mid-level experience increase applicant competition.
Medium — core data engineering skills transfer across industries, but Snowflake/cloud specialization biases fit toward similar platforms.
High — explicit 6–8 years requirement plus mandatory Snowflake, Airflow, DBT, cloud, and leadership skills.
Lead design, development, and implementation of scalable data engineering solutions using Snowflake, Apache Airflow, DBT, Matillion, and cloud-native services.
Manage multiple data engineering projects to ensure on-time delivery, quality outcomes, and alignment with business objectives while collaborating with cross-functional teams.
Lead and mentor a team of data engineers, establish coding standards, and drive platform reliability, automation, and innovation initiatives.
6-8 years of experience in Data Engineering, Data Platform Development, or related roles with proven technical project leadership.
Strong expertise in Snowflake or equivalent cloud data warehouse technologies and hands-on experience with Apache Airflow, DBT, Matillion, and ELT/ETL architectures.
Experience building cloud-native data solutions on Azure (preferred) and/or AWS with strong SQL and Python programming skills.
Bachelor's or Master's degree in Computer Science, IT, Engineering, or related field.
Experienced data engineering leader capable of driving technical decisions and influencing architecture standards in large-scale enterprise environments.
Demonstrated ability to manage complex projects and collaborate effectively across data architects, scientists, ML engineers, and business stakeholders.
Skilled in mentoring and developing engineering teams while promoting engineering excellence, automation, and continuous improvement.