Lead Staff Data Engineer - Data Platform

Company: Apna

Team: Data Platform / Engineering

Location: Bangalore

Experience : 5-7 Years of Experience

Why Join Apna

At Apna, data is central to how we build products, understand users, improve employer outcomes, power recommendations, and scale decision-making. This role gives you the opportunity to build the backbone of Apnas data platform and influence how data is used across the company.

You will work on real-world, high-scale problems across jobs, users, employers, communities, matching, growth, and AI-driven systems.

About The Role

Apna is looking for a Lead / Staff Data Engineer to build and scale our core data platform. This role will work on large-scale data pipelines, lakehouse architecture, query platforms, workflow orchestration, and data reliability systems that power analytics, product intelligence, machine learning, business dashboards, experimentation, and operational decision-making across Apna.

We are looking for someone who can think deeply about data architecture, design reliable pipelines, improve data quality, and help build a platform that can scale with Apnas growth.

What Youll Own:

You will be responsible for designing, building, and operating critical parts of Apnas data platform, including:

• 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
  
  

• 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
  
  

• 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
  
  

• 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 Apnas user, job, employer, and engagement data