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Hybrid mid-level, popular data role in Hyderabad at a known startup increases applicant competition.
Data engineering skills transfer across industries, but Airflow specialization adds moderate domain specificity.
Explicit 3-5 year requirement plus Airflow, Python, and cloud expertise creates stringent screening.
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Accountable for delivering Apache Airflow implementations and solutions to a diverse customer base including startups and Fortune 500 companies.
Responsible for architecting and optimizing scalable data engineering frameworks and pipelines using Apache Airflow and Astronomer platform.
Act as a trusted advisor, guiding customers on Airflow best practices and continuously improving technical expertise while contributing feedback to product teams.
3-5 years of data engineering experience, preferably with Apache Airflow in production environments.
Proficiency in creating DAGs, Python programming, and building custom Airflow operators and hooks.
Experience with ETL/ELT pipelines and data transformation with tools like Cloudera, Databricks, Snowflake, DBT, AWS, Azure, or Google Cloud.
Work Location: Hybrid model requiring at least 3 days per week in Hyderabad office.
Strong hands-on experience implementing and optimizing Apache Airflow solutions in production across varied customer environments.
Comfortable engaging directly with customers to deliver technical guidance and consultations related to data pipelines and Airflow usage.
Experienced in modern data engineering ecosystems, including familiarity with CI/CD pipelines, Docker, Kubernetes, and cloud-based analytics solutions.