Senior Data Engineer - SaaS (Data architecture/ Startup)

Responsibilities:

• Own end-to-end data architecture across ingestion, transformation, storage, and analytics
• Design and scale batch and streaming pipelines using Python, Airflow, Spark / PySpark, Kafka, dbt, and Debezium
• Architect and optimize data models across MongoDB, ClickHouse, and OpenSearch for high-volume ingestion and low-latency analytics
• Establish strong data quality, validation, and lineage practices using Great Expectations or similar frameworks
• Build clean, well-documented, analytics-ready datasets that power dashboards and AI workflows
• Mentor and guide a team of data engineers, setting standards and driving execution
• Partner closely with product, AI, and platform teams to enable new data use cases and faster experimentation
• Continuously improve performance, reliability, and cost efficiency on AWS

Good to have:

• 4 years of experience in data engineering/ architecture or data platform roles
• At least 2 years of experience leading or mentoring engineers
• Strong hands-on expertise in Python, Apache Airflow, Spark / PySpark, and MongoDB
• Solid understanding of ETL / ELT patterns, streaming systems, CDC, and scalable data modeling
• Experience designing systems that balance flexibility, performance, and data quality
• Working knowledge of AWS and production performance tuning
• Exposure to supply chain, procurement, pricing, or marketplace data
• Experience with data contracts, observability, or data SLAs
• Familiarity with privacy and compliance concepts such as GDPR or India DPDP