Lead / Staff Data Engineer - Data Platform

About the role

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 / Trino for 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 Airflow or similar orchestration systems. Strong knowledge of Presto / Trino or other distributed query engines. Good understanding of Apache Hudi concepts 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.

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