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Mid-level role, metro location, and a recognizable employer increase applicant competition.
Data engineering skills (Python, SQL, ETL, cloud) are transferable across industries but domain knowledge matters.
Explicit 4-6 years and mandatory AWS, Snowflake, Airflow, Python requirements enforce strict shortlisting.
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Lead and execute data engineering projects focusing on cloud platform implementations, particularly AWS and Snowflake ecosystems.
Develop and optimize data pipelines using Python, SQL, and orchestration tools like Apache Airflow, ensuring performance and cost efficiency.
Collaborate within Agile Scrum teams to deliver scalable data solutions supporting data warehousing, ETL processes, and data integration tasks.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or related field.
4-6 years of hands-on experience in data engineering or related roles, with cloud platform specialization.
Strong proficiency in Python, SQL, AWS services (S3, Glue, Lambda), Snowflake, and data pipeline orchestration tools such as Apache Airflow.
Experience in Agile Scrum processes and knowledge of data security, privacy regulations, and compliance.
Experienced data engineer with deep expertise in cloud-native data warehousing and pipeline development, especially on AWS and Snowflake platforms.
Proven capability to optimize data performance and costs while adapting workflows to evolving project requirements within cross-functional Agile teams.
Demonstrated proficiency in end-to-end data solutions including ETL, API development, and data visualization tools like Tableau or Power BI.