Senior Data Engineer

Responsibilities

• Initiate, plan, and take full ownership of data engineering projects and pipeline deliverables, from design through production, with minimal supervision.
• Design, build, and optimize high-volume ETL/ELT pipelines across marketplace listings, user behavior, and transaction data. Leverage AI coding tools (such as Claude Code) as an extended productivity assistant for boilerplate logic and DAG scaffolding, while taking full engineering ownership of pipeline accuracy, cost, and reliability
• Evaluate and pilot AI-assisted patterns and natural language interfaces to build internal tools, platform automations, and operational workflows—sharing practical findings to elevate technical leverage and execution speed across the team.
• Own the design of data warehouse and datamart structures, including performing basic DBA operations on our cloud data warehouse.
• Build and maintain streaming/real-time data pipelines to support event-based and low-latency use cases.
• Lead the architecture and delivery of components of our data warehouse migration, including data model restructuring, security, and governance.
• Partner with Data Scientists and Analysts to enable personalization, pricing intelligence, fraud detection, and other ML-driven initiatives.
• Diagnose and resolve complex pipeline failures and performance issues, and proactively identify opportunities to improve reliability across our data platform
• Define and document data processes, schemas, and workflow automation standards for the team.
• Champion data security and governance best practices across the pipelines and systems you own.
• Mentor and support the technical growth of junior and mid-level data engineers, including code review and knowledge sharing.
• Communicate technical designs, trade-offs, and project status clearly to both technical and non-technical stakeholders.

Qualifications

• 5+ years of hands-on experience in Data Engineering or a related field.
• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).
• Mastery of SQL, Linux shell scripting, and proficiency in at least one object-oriented or functional language (Python, Java, Scala, or Golang)
• Strong command of ETL/ELT concepts and orchestration tools (Apache Airflow), including comfort pairing with AI coding assistants for development, test generation, and code review — able to evaluate AI-generated pipeline code rather than accept it uncritically.
• Deep experience across relevant data management technologies and cloud services.
• Proven experience designing and optimizing data warehouses and datamarts, including DBA operations, entity relationships and normalisations.
• Hands-on experience with streaming/event-based data pipelines and real-time data processing.
• Experience working with high-volume, high-velocity data from varied and sometimes low-quality sources.
• Solid data modeling skills across SQL and NoSQL systems.
• Demonstrated ability to independently initiate, scope, and deliver projects with minimal guidance.
• Strong analytical skills, attention to detail, and excellent communication and stakeholder management skills.

 

Good to Haves

•        Applied knowledge of software design principles (e.g., Single Responsibility Principle, separation of concerns, testability, and maintainable pipeline architecture).

•        Experience working with marketplace, classified or commerce data.

•        Experience leading or contributing significantly to data warehouse planning, designs or migrations.

•        Understanding of A/B testing, data segmentations, and recommendation systems.

•        Working knowledge of machine learning pipelines and MLOps practices.

•        Understanding of, AI/ML concepts and data pipelines

Why Join Us?

•        Take ownership of large-scale data challenges in a fast-growing e-commerce marketplace.

•        Directly shape our data warehouse migration and ML enablement roadmap.

•        Opportunity to impact millions of users by improving search, recommendations, and fraud detection.

•        Hybrid work flexibility.

•        Competitive salary and benefits (health insurance, performance bonuses, learning budgets).

•        Clear path for growth into Staff Engineer / Team Lead tracks, with mentorship opportunities within the Data Engineering & Analytics team.

 

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