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Mid-level experience, metro location, and common data-engineering skillset create moderate competition.
Requires Airflow and data engineering expertise, but skills are largely transferable across industries.
Explicit 3–5 years and mandatory Airflow/Python data-engineering skills create strict screening criteria.
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Deliver and support Apache Airflow implementations across diverse customers ranging from startups to Fortune 500 enterprises.
Serve as a trusted advisor, educating customers on Airflow best practices and optimizing data workflows and configurations.
Collaborate with internal teams to design and improve engineering solutions and provide feedback to product teams based on client interactions.
3-5 years of data engineering experience with proven use of Apache Airflow in production environments.
Proficient in creating DAGs, writing Python code, and building Airflow custom operators and hooks.
Experience with ETL/ELT pipelines and related technologies like Cloudera, Databricks, Snowflake, DBT, AWS, Azure, or Google Cloud.
Required to work in a hybrid model with at least 3 days per week at Hyderabad office.
Strong expertise and continuous growth focus on Apache Airflow and Astronomer’s managed platform with ability to stay updated on latest features.
Effective communicator capable of managing customer engagements across multiple channels and delivering post-sales solutions.
Ability to assess and refactor Airflow codebases and optimize data pipelines for performance and scalability.