Sr. Data Engineer

Narvar is growing! We’re building the data infrastructure behind the post-purchase experiences of hundreds of millions of consumers. When a shopper tracks a package, initiates a return, or gets a delivery notification through one of our 1,500+ brand partners, our data systems are doing the work behind the scenes. We’re looking for a Senior Data Engineer to own and evolve the pipelines, platforms, and data products that power Narvar’s analytics, ML, and merchant-facing products.

You’ll work across the full stack of our data infrastructure, from ingestion and transformation to the analytics surfaces our merchants use every day. You’ll make architectural decisions, ship production systems at scale, and we expect you to work in an AI-native way, using agentic coding tools to increase your leverage and ship faster.

What You’ll Work On

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery
• Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features
• Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly
• Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams
• Improve data quality and reliability through testing, observability, alerting, and validation frameworks
• Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions
• Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

What We’re Looking For

We care about judgment and ownership over credentials.

You’re likely a strong fit if you:

• Have 5–8 years of experience building and operating production data systems
• Have strong SQL skills and are proficient in Python, with flexibility to pick up other languages as needed. Comfortable building and maintaining APIs.
• Have worked with modern data stacks on cloud platforms (GCP preferred, but AWS or Azure transfers well), including cloud data warehouses like BigQuery, ELT patterns, and orchestration with Airflow
• Understand data modeling deeply — dimensional modeling, slowly changing dimensions, incremental processing
• Treat data quality, lineage, and observability as first-class engineering concerns
• Communicate clearly with technical and non-technical stakeholders and are comfortable working cross-functionally
• Already use AI and agentic coding tools as a core part of how you work for planning, code generation, debugging, and code review.

Signals That You’ll Thrive Here

These aren’t hard requirements, but strong indicators:

• You’ve worked in startup or high-ownership environments where you wore multiple hats
• You’ve built or maintained embedded analytics or multi-tenant data products
• You’re excited about making data accessible to AI systems, whether that’s through better metadata, semantic layers, or preparing datasets for agentic workflows
• You care about cost optimization and data governance

Why Data Engineering at Narvar?

Because post-purchase is one of the most data-rich and underserved problem spaces in e-commerce.

We process 10+ billion consumer interactions across 1,500+ retailers, 38 countries, and 55 languages. That data spans tracking, returns, claims, notifications, and customer care. Most data engineering roles have you building pipelines for internal dashboards. Here, the data products you build are the product — merchants make decisions from the analytics we surface, and our agentic AI (Navi) reasons over the data layer you help shape. We’re at an inflection point, investing in embedded merchant analytics and LLM-readiness. You’ll get startup-level ownership with platform-level impact, on a team that values thinking, judgment, and responsibility.

We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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