Director - Data Engineering

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.