• Design, build, and maintain production-grade data pipelines using Airflow and AWS services like Lambda, DynamoDB • Own data ingestion from internal systems and third-party integrations (e.g., Google, Bing, external APIs). • Manage data storage and movement across S3, Snowflake, Snowpipe, and DynamoDB. • Write and maintain custom Python code that runs reliably in production. • Work across dev, staging, and production environments with proper deployment and rollback practices. • Partner with analytics, data science, and product teams to design reliable, usable data models. • Review code, mentor junior engineers, and help establish best practices for data quality, reliability, and observability. • Identify and fix performance, cost, or reliability issues in existing pipelines.

• Build and maintain data pipelines for ingestion, transformation, and reporting with guidance from senior team members • Support Data Science and Business Intelligence teams with data preparation and quality assurance • Help monitor and troubleshoot data pipeline issues under supervision • Contribute to improving data quality and pipeline reliability initiatives • Learn to work with distributed systems and cloud technologies • Write clean, well-documented code following established team practices • Develop well-documented, tested data pipelines following team coding standards and best practices • Participate in code reviews and collaborative design sessions to continuously improve your technical skills • Work closely with product stakeholders to understand business requirements and translate them into technical solutions • Support data quality monitoring and help identify and resolve data issues • Contribute to team documentation and knowledge sharing initiatives • Stay curious and continuously learn new technologies and techniques

• Develop and optimize data pipelines • Build and maintain analytical, high-performance, and well-documented data models in BigQuery • Implement and manage monitoring and testing frameworks to ensure data quality and integrity • Lead the definition and enforcement of data governance policies, including organization and cataloging of data assets • Serve as a technical reference, promoting best practices in data engineering and data modeling across the team • Collaborate with business stakeholders to understand requirements and ensure data is accessible and documented

• Develop end-to-end ELT pipelines that integrate data from multiple sources to power a reliable and scalable data platform • Design and build high-performance, reusable data models that efficiently handle large datasets and support analytics, marketing, and machine learning use cases • Create and maintain APIs that enable seamless data sharing across systems, including APIs for deploying and serving machine learning models in production • Partner with engineering, data, and business teams to drive and deliver impactful data engineering initiatives • Contribute meaningfully to the design and implementation of core data infrastructure decisions