Associate Architect

About Us DataWeave is a SaaS-based digital commerce analytics platform that empowers retailers with competitive intelligence and enables consumer brands to optimize their digital shelf performance at a global scale. Powered by proprietary AI technology, DataWeave processes over 500+ billion data points across 400,000+ brands, 4,000+ websites, and more than 20 industry verticals. Our customers include leading global retailers and brands such as Nordstrom, Overstock, The Home Depot, Mars, Bush Brothers, Mondelez, and Pernod Ricard. We are a globally distributed team of 220+ engineers, product managers, and eCommerce experts with our technology hub based in Bangalore. Role Overview We are seeking a Tech Lead / Associate Architect – Data Engineering to drive the design and implementation of scalable data platforms, ETL pipelines, and distributed data processing systems. The role involves close collaboration with product, data science, and analytics teams to build reliable data foundations that power business insights at scale. Key Responsibilities Design, build, and own end-to-end ETL pipelines for large-scale data ingestion, transformation, and processing Architect scalable workflows integrating structured and unstructured data sources Optimize ETL frameworks for performance, scalability, and cost efficiency Lead integrations with Snowflake, Databricks, or similar analytical data platforms Manage cloud-native deployments using Kubernetes and CI/CD pipelines Ensure data quality, governance, monitoring, and reliability across data pipelines Collaborate with product, data science, and analytics teams to translate requirements into engineering solutions Mentor and guide engineers on data engineering best practices, code quality, and architecture Required Qualifications 6–10 years of experience in data engineering or related roles Bachelor’s or Master’s degree in Computer Science, Information Systems, or equivalent experience Strong expertise in Python with experience building production-grade systems Hands-on experience with distributed data frameworks such as PySpark and Airflow Solid experience with AWS services including S3, Athena, Lambda, and Step Functions Strong experience with data warehousing platforms such as Snowflake or Databricks Working knowledge of Kubernetes, Helm, and containerized deployments Exposure to NoSQL databases such as MongoDB, DynamoDB, or Cassandra Experience in query optimization, data partitioning, and large-scale data performance tuning Familiarity with Git, CI/CD pipelines, and Infrastructure-as-Code tools such as Terraform or CloudFormation Preferred Qualifications Experience building data platforms for analytics, reporting, or AI/ML workloads Understanding of modern data lakehouse architectures Exposure to data modeling and ELT design principles Strong communication skills and ability to work with cross-functional stakeholders