Responsibilities:Own end-to-end data architecture across ingestion, transformation, storage, and analyticsDesign and scale batch and streaming pipelines using Python, Airflow, Spark / PySpark, Kafka, dbt, and DebeziumArchitect and optimize data models across MongoDB, ClickHouse, and OpenSearch for high-volume ingestion and low-latency analyticsEstablish strong data quality, validation, and lineage practices using Great Expectations or similar frameworksBuild clean, well-documented, analytics-ready datasets that power dashboards and AI workflowsMentor and guide a team of data engineers, setting standards and driving executionPartner closely with product, AI, and platform teams to enable new data use cases and faster experimentationContinuously improve performance, reliability, and cost efficiency on AWSGood to have:4 years of experience in data engineering/ architecture or data platform rolesAt least 2 years of experience leading or mentoring engineersStrong hands-on expertise in Python, Apache Airflow, Spark / PySpark, and MongoDBSolid understanding of ETL / ELT patterns, streaming systems, CDC, and scalable data modelingExperience designing systems that balance flexibility, performance, and data qualityWorking knowledge of AWS and production performance tuningExposure to supply chain, procurement, pricing, or marketplace dataExperience with data contracts, observability, or data SLAsFamiliarity with privacy and compliance concepts such as GDPR or India DPDP
Job Title
Senior Data Engineer - SaaS (Data architecture/ Startup)