About the team and what we'll build togetherAt Kobie, we turn our client's loyalty data into actionable intelligence that drives enterprise value. Our Data Engineering team builds and maintains the operational data foundation that powers real-time dashboards, client-facing reporting, and emerging AI-driven products, serving a growing portfolio of enterprise loyalty program clients across some of the most recognized brands in the world.
As a Senior Data Engineer, you will be a key technical contributor and operational owner within our data engineering function. You will bring deep Snowflake expertise and strong engineering instincts to own production data pipelines, lead client implementations, and help us evolve our data architecture to support event-driven and AI-powered workflows, all within a multi-hundred terabyte Snowflake environment where the data problems are as complex as the brands behind them.
How you will make an impact - Serve as the operational backbone of Kobie's data platform, ensuring the ETL/ELT pipelines, dimensional models, and Snowflake environments that power client reporting and dashboards run reliably, at scale, every day.
- Translate client loyalty program requirements into production-ready data warehouse structures - from source system analysis and dimensional modeling through to the KALC (Kobie Alchemy Loyalty Cloud) platform tables and extracts that downstream reporting and client services teams depend on.
- Diagnosing and resolve data pipeline failures quickly enough that clients and internal stakeholders rarely notice they happened by bringing the debugging fluency and Snowflake depth to contain impact and prevent recurrence.
- Build a data architecture that can absorb change by redesigning Kobie's data mart to support an incoming event-driven, domain-driven application layer without breaking the operational foundation already in production.
- Deliver the data infrastructure that makes AI-powered loyalty products possible. Not by building the models, but by building the reliable, well-governed platform that Kobie's innovation team needs to produce them.
- Leave every pipeline more trustworthy than you found it, through automated testing, audit logging, CI/CD discipline, and documentation that makes the platform easier for the next engineer to own.
What you need to be successfulRequired
- 6+ years of Data Engineering experience designing and operating production grade data pipelines.
- 3+ years of hands-on Snowflake experience as a primary data warehousing solution, with deep fluency across data sharing, data clean rooms, marketplace, and Snowpark.
- Strong proficiency in SQL, Python, and JavaScript for data transformation, pipeline development, and automation scripting.
- Demonstrated CI/CD experience using GitHub and GitHub Actions
- Experience with data replication tools (Kafka, GoldenGate, HVR, Qlik Replicate or similar)
- Deep understanding of Kimball dimensional modeling: star schemas, slowly changing dimensions, snapshot and transaction fact tables.
- Experience integrating a wide range of data sources, including APIs, messaging systems, and streaming platforms.
- Solid grounding in OLTP, Data Vault, and data warehouse architecture patterns, with the ability to assess source systems and translate business requirements into dimensional models.
- Experience designing event-driven architectures and understanding how they shape data pipeline design.
- Familiarity with domain-driven design concepts and how application architecture changes flow downstream into the warehouse.
- Cloud experience with Azure and/or AWS.
- Comfort working independently across concurrent projects in an Agile environment, with strong communication to non-technical data stakeholders.
Strongly Preferred
- Prior experience in a terabyte-scale, multi-client data warehouse environment, you've worked at this kind of scale before and know what it demands.
- Experience with data replication tool Qlik Replicate, preferred
- Familiarity with Apache NiFi or comparable data flow orchestration tools, preferred
- Exposure to AI workflow integration within a data warehousing context, or experience building pipelines that serve downstream machine learning use cases.
