• Building scalable batch and near-real-time data pipelines across product, business, growth, and ML use cases.
• Designing and improving our lakehouse architecture using technologies likeApache Hudi.
• Working with query engines such asPresto / Trinofor large-scale analytical workloads.
• Building and maintaining orchestration workflows usingApache Airflow.
• Creating reusable data models, curated datasets, and reliable data marts for analytics and product teams.
• Improving data platform reliability, observability, SLA tracking, lineage, and data quality checks.
• Optimizing storage, compute, query performance, and pipeline costs.
• Partnering with product, analytics, ML, and backend engineering teams to understand data needs and convert them into scalable platform solutions.
• Driving engineering standards around data modeling, schema evolution, partitioning, deduplication, backfills, replayability, and pipeline ownership.
• Mentoring data engineers and influencing architecture decisions across teams.

What We’re Looking For

Must Have

• Strong experience indata engineering, preferably at scale.

• Hands-on experience withApache Airflowor similar orchestration systems.

• Strong knowledge ofPresto / Trinoor other distributed query engines.

• Good understanding ofApache Hudiconcepts such as:

• Copy-on-write vs merge-on-read

• Upserts and deletes

• Incremental reads

• Compaction

• Clustering

• Timeline and commits

• Schema evolution

• Partitioning strategy

• Strong knowledge of distributed data processing and storage systems.

• Ability to design and build reliable ETL / ELT pipelines.

• Strong SQL skills and ability to debug complex data issues.

• Good understanding of different data architectures, including:

• Data warehouse

• Data lake

• Lakehouse

• Lambda architecture

• Kappa architecture

• Medallion architecture

• Event-driven data architecture

• Experience with data modeling for analytics and reporting.

• Strong programming skills in at least one language such asPython, Java, or Scala.

• Ability to reason about trade-offs between freshness, cost, reliability, latency, and complexity.

• Strong debugging and production ownership mindset.

Good to Have

• Experience with Kafka, Spark, Flink, Hive, Iceberg, Delta Lake, or BigQuery.
• Experience building internal data platforms or self-serve data infrastructure.
• Experience with data quality frameworks such as Great Expectations, Deequ, Soda, or custom validation systems.
• Exposure to ML feature pipelines or feature stores.
• Experience with metadata management, data catalogs, lineage, and governance.
• Experience with cloud infrastructure such as AWS, GCP, or Azure.
• Understanding of privacy, compliance, PII handling, and access control in data systems.

What Success Looks Like
In this role, success means:

• Critical business and product datasets are reliable, discoverable, and trusted.
• Pipelines are observable, recoverable, and have clear SLAs.
• Query performance improves across major analytical workloads.
• Data freshness and quality issues reduce significantly.
• Teams can build on top of the data platform faster without reinventing pipelines.
• The platform can scale with Apna’s user, job, employer, and engagement data.

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