Senior Data Engineer / SSE

Role: Senior Data Engineer / SSE Requirement: 1 Team: Data Platform / Engineering Location: Work from Office - Domlur, Bangalore (5 days / week) Experience : 4-6 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 Senior Software 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. Requirements 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 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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