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Tier‑1 employer plus mid-level experience increases competition, but specialist Snowflake/DBT/Kafka skills moderate it.
Tooling-specific data engineering is transferable across industries but requires domain and tooling knowledge, so medium.
Mandatory 2–5 years plus specific Snowflake/ETL/Kafka/Python skills imposes moderate filtering.
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Develop and maintain scalable data processing platforms to support Corporate Functional Data IT.
Ensure on-time project delivery, including development, testing, and deployment of data-related applications in on-premises or cloud environments.
Maintain and optimize multiple data pipelines, integrate advanced technology for faster insights, and contribute to automation framework development and maintenance.
Bachelor’s degree in engineering, technology, or related field.
2–5 years of experience in Data Warehousing with strong understanding of ETL processes and tools.
Experience with cloud data platforms (e.g., Snowflake), ETL tools (Informatica, DBT), and data streaming technologies (Kafka).
Proficiency in Unix shell scripting, Python, SQL, and familiarity with Git, CI/CD pipelines, and Agile methodologies.
Experienced in full software development lifecycle for data applications, including cloud and on-premises environments.
Strong collaborator capable of aligning technical execution with operational goals across engineering and platform teams.
Familiar with real-time data processing, data governance, security, and automation in data engineering contexts.