Lead / Staff Data Engineer - Data Platform

Company: Apna

Team: Data Platform / Engineering

Location: Bangalore (Work from Office - Domlur)

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 Rol

eApna 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 growt

h.
What You’ll O

wn:You will be responsible for designing, building, and operating critical parts of Apna’s data platform, includi

• ng:Building scalable batch and near-real-time data pipelines across product, business, growth, and ML use cas
• es.Designing and improving our lakehouse architecture using technologies likeApache Hu
• di.Working with query engines such asPresto / Trinofor large-scale analytical workloa
• ds.Building and maintaining orchestration workflows usingApache Airfl
• ow.Creating reusable data models, curated datasets, and reliable data marts for analytics and product tea
• ms.Improving data platform reliability, observability, SLA tracking, lineage, and data quality chec
• ks.Optimizing storage, compute, query performance, and pipeline cos
• ts.Partnering with product, analytics, ML, and backend engineering teams to understand data needs and convert them into scalable platform solutio
• ns.Driving engineering standards around data modeling, schema evolution, partitioning, deduplication, backfills, replayability, and pipeline ownersh
• ip.Mentoring data engineers and influencing architecture decisions across tea

ms.
What We’re Looking

ForMust

• HaveStrong experience indata engineering, preferably at sc
• ale.Hands-on experience withApache Airflowor similar orchestration syst
• ems.Strong knowledge ofPresto / Trinoor other distributed query engi
• nes.Good understanding ofApache Hudiconcepts such
• as:Copy-on-write vs merge-on-
• readUpserts and del
• etesIncremental r
• eadsCompac
• tionCluste
• ringTimeline and com
• mitsSchema evolu
• tionPartitioning stra
• tegyStrong knowledge of distributed data processing and storage syst
• ems.Ability to design and build reliable ETL / ELT pipeli
• nes.Strong SQL skills and ability to debug complex data iss
• ues.Good understanding of different data architectures, includ
• ing:Data wareh
• ouseData
• lakeLakeh
• ouseLambda architec
• tureKappa architec
• tureMedallion architec
• tureEvent-driven data architec
• tureExperience with data modeling for analytics and report
• ing.Strong programming skills in at least one language such asPython, Java, or Sc
• ala.Ability to reason about trade-offs between freshness, cost, reliability, latency, and complex
• ity.Strong debugging and production ownership mind

set.
Good to

• HaveExperience with Kafka, Spark, Flink, Hive, Iceberg, Delta Lake, or BigQ
• uery.Experience building internal data platforms or self-serve data infrastruc
• ture.Experience with data quality frameworks such as Great Expectations, Deequ, Soda, or custom validation sys
• tems.Exposure to ML feature pipelines or feature st
• ores.Experience with metadata management, data catalogs, lineage, and govern
• ance.Experience with cloud infrastructure such as AWS, GCP, or A
• zure.Understanding of privacy, compliance, PII handling, and access control in data sys

tems.
What Success Look

s LikeIn this role, success

• means:Critical business and product datasets are reliable, discoverable, and tr
• usted.Pipelines are observable, recoverable, and have clear
• SLAs.Query performance improves across major analytical work
• loads.Data freshness and quality issues reduce signific
• antly.Teams can build on top of the data platform faster without reinventing pipe
• lines.The platform can scale with Apna’s user, job, employer, and engagement