How You’ll Make an Impact

Data engineering (Responsibilities & expectations):

• 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.

Process (Standards, governance and operational excellence):

• 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.

Program management (Delivery, stakeholder engagement and scaling):

• Own end-to-end program delivery for large, multi-team initiatives-set milestones, resource plans, risk registers, and communication cadences.

• Coordinate cross-functional stakeholders including product, analytics, ML/AI, security, legal, and infrastructure to align roadmaps and priorities.

• Manage portfolio trade-offs: technical debt vs. new features, cost vs. performance, central platform vs. team autonomy.

• Drive capacity planning and resource allocation across multiple projects; balance short-term business needs with platform investments.

• Provide transparent status reporting to senior leadership, present roadmaps, and translate technical trade-offs into business impact and ROI metrics.

Innovation (Strategy and value creation):

• 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.

In your first 90 days

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)..

What you need to be successful

• 15+ years of experience in data engineering, with at least 5 years leading teams and large-scale data platform programs.

• Hands-on expertise with Snowflake (preferred) or equivalent experience with Azure Databricks or AWS Redshift at scale (production data platforms, multi-tenant pipelines).

• Deep understanding of cloud data architectures, data modeling, performance tuning, security, and cost management.

• Proven program management skills running complex, cross-functional initiatives and delivering measurable business impact.

• Strong people leadership: hiring, coaching, and building high-performing engineering teams.

• Excellent communication and stakeholder management; comfortable presenting to senior executives.

• Bachelor’s or Master’s in Computer Science, Engineering, or related field (or equivalent experience).

Bonus Skills:

• Experience with data mesh, feature stores, or ML infrastructure.

• Experience with dbt, Delta Lake, Kafka/Event Hubs, Airflow, or similar orchestration tools.

• Familiarity with feature stores, MLOps, and GenAI/RAG integration patterns.

• Certifications: SnowPro or equivalent, cloud provider certifications.