Data Architect - Python/Spark

Role Overview: As a Data Architect, you will be responsible for designing and owning the data architecture essential for analytics, customer-facing insights, and AI models. You will lead a small but impactful team of data engineers while actively participating in design and implementation. This position requires strategic thinking along with the ability to write production-grade code. Key Responsibilities: - Own the end-to-end data architecture encompassing ingestion, transformation, storage, and analytics. - Design and scale batch and streaming pipelines using tools like Python, Airflow, Spark / PySpark, Kafka, dbt, and Debezium. - Architect and optimize data models for MongoDB, ClickHouse, and OpenSearch to handle high-volume ingestion and low-latency analytics. - Establish robust data quality, validation, and lineage practices utilizing frameworks like Great Expectations. - Develop clean, well-documented datasets tailored for analytics and AI workflows. - Provide mentorship and guidance to a small team of 23 data engineers, defining standards and fostering execution. - Collaborate closely with product, AI, and platform teams to facilitate new data use cases and expedite experimentation. - Continuously enhance performance, reliability, and cost efficiency on AWS. Qualifications Required: - 7-12 years of experience in data engineering or data platform roles. - Minimum 2 years of experience in leading or mentoring engineers. - Proficiency in Python, Apache Airflow, Spark / PySpark, and MongoDB. - Strong grasp 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. Additional Company Details: The job description does not include any additional details about the company. Role Overview: As a Data Architect, you will be responsible for designing and owning the data architecture essential for analytics, customer-facing insights, and AI models. You will lead a small but impactful team of data engineers while actively participating in design and implementation. This position requires strategic thinking along with the ability to write production-grade code. Key Responsibilities: - Own the end-to-end data architecture encompassing ingestion, transformation, storage, and analytics. - Design and scale batch and streaming pipelines using tools like Python, Airflow, Spark / PySpark, Kafka, dbt, and Debezium. - Architect and optimize data models for MongoDB, ClickHouse, and OpenSearch to handle high-volume ingestion and low-latency analytics. - Establish robust data quality, validation, and lineage practices utilizing frameworks like Great Expectations. - Develop clean, well-documented datasets tailored for analytics and AI workflows. - Provide mentorship and guidance to a small team of 23 data engineers, defining standards and fostering execution. - Collaborate closely with product, AI, and platform teams to facilitate new data use cases and expedite experimentation. - Continuously enhance performance, reliability, and cost efficiency on AWS. Qualifications Required: - 7-12 years of experience in data engineering or data platform roles. - Minimum 2 years of experience in leading or mentoring engineers. - Proficiency in Python, Apache Airflow, Spark / PySpark, and MongoDB. - Strong grasp 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. Additional Company Details: The job description does not include any additional details about the company.