Data Architect Lead

As the Data Architect, you will be responsible for owning the end-to-end data architecture, including ingestion, transformation, storage, and analytics. Your role will involve designing and scaling batch and streaming pipelines using tools such as Python, Airflow, Spark / PySpark, Kafka, dbt, and Debezium. Key Responsibilities: - Architect and optimize data models across MongoDB, ClickHouse, and OpenSearch to handle high-volume ingestion and support low-latency analytics. - Establish robust data quality, validation, and lineage practices utilizing frameworks like Great Expectations. - Develop clean, well-documented datasets that are analytics-ready, enabling the creation of dashboards and AI workflows. - Mentor and guide a team of data engineers, setting standards and driving execution. - Collaborate closely with product, AI, and platform teams to facilitate new data use cases and accelerate experimentation. - Continuously enhance performance, reliability, and cost efficiency on AWS. Qualifications Required: - 4 years of experience in data engineering/architecture or data platform roles. - At least 2 years of experience in 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 in 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. This job provides an exciting opportunity to work on diverse data challenges, mentor a team, and collaborate with various stakeholders to drive impactful data solutions. As the Data Architect, you will be responsible for owning the end-to-end data architecture, including ingestion, transformation, storage, and analytics. Your role will involve designing and scaling batch and streaming pipelines using tools such as Python, Airflow, Spark / PySpark, Kafka, dbt, and Debezium. Key Responsibilities: - Architect and optimize data models across MongoDB, ClickHouse, and OpenSearch to handle high-volume ingestion and support low-latency analytics. - Establish robust data quality, validation, and lineage practices utilizing frameworks like Great Expectations. - Develop clean, well-documented datasets that are analytics-ready, enabling the creation of dashboards and AI workflows. - Mentor and guide a team of data engineers, setting standards and driving execution. - Collaborate closely with product, AI, and platform teams to facilitate new data use cases and accelerate experimentation. - Continuously enhance performance, reliability, and cost efficiency on AWS. Qualifications Required: - 4 years of experience in data engineering/architecture or data platform roles. - At least 2 years of experience in 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 in 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. This job provides an exciting opportunity to work on diverse data challenges, mentor a team, and collaborate with various stakeholders to drive impactful data solutions.