Data Engineer

About the Role We’re looking for a driven and self-motivated Data Engineer to join a fast-paced, collaborative team building scalable, production-grade data solutions. This is a great opportunity for someone who thrives in ambiguity, takes ownership end-to-end, and enjoys partnering across teams to deliver impactful data products. You’ll play a key role in designing, building, and optimizing data pipelines that power critical business insights—while ensuring high standards of performance, scalability, and data quality. Key Responsibilities • Design, build, and maintain scalable ETL/ELT data pipelines • Perform data validation, debugging, and root cause analysis to ensure data integrity • Partner with cross-functional teams (data, product, engineering) to deliver data solutions aligned with business needs • Support and troubleshoot production data issues with minimal disruption • Continuously improve existing data systems and contribute to enhancements • Maintain documentation, governance, and best practices across workflows • Deliver high-quality, production-ready solutions focused on performance and reliability Required Skills & Experience • Advanced SQL expertise (complex queries, optimization, data validation) • Hands-on experience with Google Cloud Platform (GCP), especially BigQuery • Strong proficiency in Python and/or Node.js for data processing and automation • Experience building and maintaining data pipelines (ETL/ELT) • Strong problem-solving skills with the ability to work independently • Excellent communication and collaboration skills in cross-functional environments Core Expectations • Self-starter who can take ambiguous problems and drive them to resolution • Strong sense of ownership and accountability • Clear and effective communication around progress, risks, and dependencies • Focus on system reliability, scalability, and performance • Commitment to data accuracy and operational excellence Nice to Have • Experience with Airflow, dbt, or similar orchestration tools • Exposure to large-scale data environments and cloud-native architectures • Background in data modeling, warehousing, or analytics platforms