VP of Technology at Lyka managing Engineering, Data, and IT functions in a pet wellness startup. Driving technology strategies for a fast - growing company focused on improving pet health.
About the role
- Vice President of Enterprise Data Platform leading design and operation of enterprise data ecosystem. Overseeing data engineers and ensuring compliance and quality standards.
Responsibilities
- Architect the Enterprise Data Platform: Define and maintain a reference architecture spanning ingestion, storage, compute, modeling, quality, observability, orchestration, and serving layers.
- Build Scalable Pipelines: Design and govern resilient pipelines from business applications into the enterprise data platform and downstream analytics services, ensuring schema drift tolerance and backward compatibility.
- Leverage Spark and PySpark for distributed processing, ETL optimization, and scalable ML workflows.
- Establish Enterprise Data Standards: Publish and maintain a governed enterprise data model and glossary, including SCD2 dimensions, point-in-time facts, conformed dimensions, lineage, SLAs, and usage policies.
- Implement SOX-Grade Controls: Deliver immutable logging, segregation of duties, maker-checker workflows, and reconciliation processes to ensure compliance and audit readiness.
- Expand compliance to include discovery and classification of PII and other sensitive data, encryption/masking, access controls, third-party risk, and audit-ready logging.
- Create 3rd Party Data Hub: Standardize intake patterns (SFTP, APIs, managed portal extracts) and enforce versioned data contracts per source for consistent 3rd party data onboarding.
- Partner Across Integration & Analytics: Collaborate with Application and Data Integration teams for API scalability, idempotent event processing, and batch patterns for large carrier files.
- Enable Secure Access & Hierarchies: Deliver a Hierarchy Service and enforce role-based and attribute-based access across systems and data domains.
- Power Advanced Analytics & AI: Operationalize workflows and model-serving capabilities to enable anomaly detection, enrichment, and mapping to accelerate AI adoption.
- Partner directly with Applied AI Engineering to design and operationalize the enterprise feature store for ML feature reuse and governance.
- Partner on Data Governance: Work closely with the Head of Data Governance to implement data quality frameworks and ensure metadata completeness across domains.
- Mentoring and Upskilling: Build a learning culture by coaching engineers on Spark and PySpark, cloud-native data engineering, observability, security, and cost-aware design.
- Provide technical reviews, pairing, and certification pathways to elevate team capabilities.
- Migrate from On-prem: Execute a phased migration from on-prem ETL to cloud-native pipelines, retiring technical debt while maintaining business continuity and SLAs.
- Sequence workloads by criticality, implement dual-run cutovers, and decommission legacy systems with clean lineage and documentation.
- Cost Optimization and Performance Management: Implement FinOps practices for cost baselining, right-sizing, autoscaling, and job-level cost allocation.
- Govern workloads with cluster policies, quotas, and prioritization.
- Optimize Spark and PySpark jobs for performance and cost efficiency.
Requirements
- 10+ years leading data engineering and architecture for complex, multi-system enterprises.
- Hands-on expertise with Spark and PySpark for distributed compute, ETL optimization, and scalable ML data pipelines.
- Experience with modern data platforms such as Databricks or Microsoft Fabric for efficient pipelines and analytics enablement.
- Proven success delivering governed data platforms and semantic layers at scale.
- Deep expertise in dimensional modeling (SCD2, point-in-time facts, conformed dimensions).
- Experience with data quality frameworks, observability tooling, schema registry, and data contracts.
- Strong background implementing SOX-grade controls and sensitive-data protection standards (PII discovery, classification, encryption/masking, access controls, audit logging).
- Demonstrated leadership managing multi-disciplinary engineering teams and vendor partners.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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