Data Engineering Lead

As Data Architect, you will design and own the data architecture that powers analytics, customer-facing insights, and AI models. You will lead a small, high-impact team of data engineers while staying deeply involved in design and implementation. This is a builder role. You will be expected to think strategically, but also write production-grade code.
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 optimise 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 small team of 2-3 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.
Requirements:
5-8 years of experience in data engineering or data platform roles.
At least 1+ year 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 modelling.
Experience designing systems that balance flexibility, performance, and data quality.
Working knowledge of AWS and production performance tuning.
Nice to Have:
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.

Experience
4-8 yrs