Senior Software Engineer / SSE

Job description

**Company:** Apna **Role:** Senior Software Engineer / SSE **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 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.