Director - Data Engineering

Kobie, a 35-year veteran of the loyalty industry, a multi-year Forrester Leader, and USA Top Workplace is expanding its global footprint by establishing a Tech Hub in India. Kobie partners with global brands to build deep connections with their customers through personalized, data-driven loyalty experiences and has a mission of growing enterprise value through loyalty. The Tech Hub will serve as a Global Capabilities Center for a broad range of technology roles, and this is your chance to play a pivotal role in shaping our presence in India. Join us as we continue to lead in loyalty, delivering innovative customer experiences for some of the world’s most recognized brands while working alongside some of the best and brightest in loyalty. 

 

• →

  Lead design and delivery of enterprise data platforms on Snowflake including data ingestion, storage architecture, consumption patterns, and cost optimization. 

• →

  Own architecture decisions for large-scale batch and streaming data pipelines, Data modeling (star, snowflake, dimensional), and semantic layers to support BI, ML, and Analytics. 

• →

  Ensure high standards for data quality, lineage, observability, schema evolution, and metadata management (catalogs, documentation). 

• →

  Champion secure-by-design implementation: access control (roles/policies), dynamic data masking, encryption, and compliance with data governance and regulatory requirements. 

• →

  Drive performance tuning and cost control measures for Snowflake (clustering, micro-partitions, materialized views, warehouse sizing, query profiling). 

• →

  Hire, mentor, and grow a high-performing engineering organization: senior engineers, architects, data pipeline owners, and DevOps/Platform engineers. 

• Define and enforce engineering best practices: CI/CD for data pipelines, Git-based workflows, code reviews, testing frameworks, and automated deployments for Snowflake objects. 

• Implement data lifecycle and cost governance policies (storage retention, data tiering, rightsizing compute, budget tracking). 

• Partner with Data Governance and Privacy teams to operationalize data classification, cataloging, and data stewardship workflows. 

• Implement a metrics-driven culture: define KPIs for platform reliability, pipeline throughput, query performance, and business adoption; run regular reviews and continuous improvement cycles. 

• Define long-term data platform vision: cloud-native data patterns, data mesh/mesh-like designs, Lakehouse architectures (Delta Lake), semantic layers, and self-serve analytics. 

• Evaluate and incubate emerging technologies (vector DBs, streaming analytics, dbt/transform frameworks, generative AI augmentation for analytics) to accelerate analytics and ML value. 

• Promote experimentation: small, fast pilots to validate new tools/approaches and scale successful patterns across the organization. 

• Build partnerships with cloud providers and ecosystem vendors (Snowflake, Databricks, AWS) to leverage new features and cost optimizations. 

• Foster a culture of continuous learning: internal Tech Talks, architecture reviews, and knowledge sharing. 

By the end of your first 90 days, you will have delivered at least one production-grade platform or process improvement end-to-end—examples include a Terraform-managed Snowflake networking and access environment, a hardened CI/CD pipeline for Snowflake objects and data pipelines, or a monitored Snowflake deployment pattern with automated cost and performance observability. You will be participating in the on-call rotation, have authored or enhanced at least one runbook or incident playbook for pipeline/platform operations, and be prepared with a prioritized recommendation for the next reliability and scalability investment (technical approach, estimated effort, and expected business impact)..