Job Description

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 Apna’s 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 Apna’s growth.

What You’ll Own:

You will be responsible for designing, building, and operating critical parts of Apna’s 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 like Apache Hudi
• Working with query engines such as Presto / Trinofor large-scale analytical workloads.
• Building and maintaining orchestration workflows using Apache 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 in data engineering, preferably at scale.
• Hands-on experience with Apache Airflowor similar orchestration systems.
• Strong knowledge of Presto / Trinoor other distributed query engines.
• Good understanding of Apache 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 as Python, 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.