Senior Data Engineer / SSE

Job description

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.