Data Engineer, ALT

• Are you a data engineer who thrives on building robust pipelines and solving complex data challenges?
• Care deeply about data quality and understand that extraction is only the beginning - the real value comes from delivering clean and usable data
• Thrive in a startup environment where you can make immediate impact and own critical systems end-to-end
• Take an incremental approach to improvements, preferring evidence-based decisions over wholesale replacements
• Are intellectually curious about how data flows through systems and passionate about automation opportunities
• Want to work at the intersection of data engineering and product, understanding how your pipelines directly impact business outcomes
• 3-4 years of experience in data engineering or related fields
• Strong Python proficiency with at least 3 years of hands-on experience
• Proven experience with large-scale data processing using dataframe technologies (Pandas, Polars, PySpark, or similar)
• Hands-on experience with pipeline orchestration tools (Airflow, Dagster, or similar DAG-based systems)
• Track record of owning at least one data pipeline end-to-end within the past 2 years
• Solid SQL skills for data analysis and transformation
• Previous startup experience - you understand the pace and adaptability required in a fast-moving environment
• A pragmatic mindset focused on delivering value incrementally rather than pursuing perfection

Desirable

• In this role, you'll own Alt's critical data infrastructure that powers our pricing model and marketplace insights by ingesting transaction and listing data from dozens of external marketplaces
• You'll be responsible for ensuring our data pipelines deliver fresh, accurate information that drives pricing decisions, market analytics, and business intelligence across the entire platform
• Design, optimize and own data pipelines that scrape, process and ingest transaction and listing data from major auction houses and marketplaces
• Build comprehensive monitoring and alerting systems to track latency, uptime, and coverage metrics across all data sources
• Continuously improve our data infrastructure by modernizing storage and processing technologies, reducing manual interventions, and optimizing for cost, performance, and reliability
• Partner with internal teams to understand data usage patterns and ensure pipelines deliver clean, standardized data that meets product requirements