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Mid-level data engineer in Bangalore with broad platform skills and 4-6 years attracts many applicants.
Data engineering skills are transferable, but platform-specific Hudi/Presto requirements increase domain sensitivity.
Explicit 4-6 years plus mandatory Hudi, Airflow, Presto and data-engineering experience.
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Design, build, and operate scalable batch and near-real-time data pipelines across product, business, growth, and ML use cases.
Develop and improve lakehouse architecture using Apache Hudi, query platforms like Presto/Trino, and manage workflow orchestration via Apache Airflow.
Ensure data platform reliability, observability, SLA tracking, data quality checks, and optimize storage, compute, and pipeline costs while mentoring data engineers.
4-6 years of experience in data engineering at scale.
Strong hands-on experience with Apache Airflow and distributed query engines such as Presto or Trino.
Good understanding of Apache Hudi including copy-on-write/merge-on-read, upserts/deletes, incremental reads, compaction, clustering, schema evolution, and partitioning strategies.
Proficiency in SQL and programming in Python, Java, or Scala.
Experienced in designing and building reliable ETL/ELT pipelines and scalable data architectures (data lake, lakehouse, lambda architectures).
Strong production ownership mindset with ability to reason trade-offs among freshness, cost, reliability, and latency.
Able to collaborate and influence cross-functional teams (product, analytics, ML, engineering) to translate data needs into scalable platform solutions.