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Mid-level Bangalore role with common experience band but niche data-platform tech reduces applicant density.
Data platform expertise transferable across industries but requires specific tool and architecture experience.
Explicit 4–6 years and mandatory data-platform stack (Airflow, Presto, Hudi) make filters strict.
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Design, build, and operate scalable batch and near-real-time data pipelines supporting product, business, growth, and ML use cases.
Develop and improve Apna’s lakehouse architecture using Apache Hudi, manage large-scale query platforms with Presto/Trino, and orchestrate workflows using Apache Airflow.
Enhance data platform reliability, observability, SLA tracking, and cost optimization while partnering across teams to meet data needs and mentoring data engineers.
4-6 years of experience in data engineering.
Hands-on experience with Apache Airflow or similar orchestration systems.
Strong knowledge of Presto/Trino or other distributed query engines and Apache Hudi concepts.
Proficient in at least one programming language such as Python, Java, or Scala with strong SQL skills.
Experienced in designing and operating scalable and reliable ETL/ELT pipelines in a high-scale production environment.
Deep understanding of modern data architectures (lakehouse, lambda, kappa, medallion) and trade-offs involving freshness, cost, reliability, and latency.
Skilled in cross-team collaboration to translate data needs into scalable solutions and capable of leading platform architecture decisions with mentorship responsibility.