Senior Data Engineer / SSE

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.

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.

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.