• Oversee and optimize the Denodo platform, defining best practices for development, governance, and performance. • Design and support new data integrations, ensuring alignment with the established architecture and standards. • Serve as the technical point of reference for the team on Denodo and BigQuery-related inquiries. • Define integration and optimization patterns across technologies such as Denodo, Snowflake, BigQuery, DBT, and data orchestration tools.

• Own and evolve the data platform - the BigQuery warehouse, dbt transformation layers, Airflow / Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric. • Build and operate the ML platform - training pipelines (Kubeflow on Vertex AI), model serving (FastAPI behind Vertex endpoints), CI/CD, containerization and typed contracts. • Take operational ownership of model-serving infrastructure so reliability isn't carried by the data scientists alone. • Harden and standardize the data models the business depends on - improving schemas, fixing data-quality issues and establishing trustworthy source-of-truth feeds. • Establish data governance and observability - bring data that lives outside the warehouse under proper governance and build operational metrics for products that don't yet have them. • Standardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt. • Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients).

Manage documents and data for litigation support, ensuring compliance with ESI specifications. Collaborate with attorneys to track and process incoming documents, and perform quality checks on exported data for legal proceedings.

• Own the enterprise data platform vision and roadmap centered on Snowflake • Define and maintain logical and physical data architecture (raw, staging, curated layers) • Establish platform standards for ingestion, modeling, and consumption • Ensure platform readiness for BI, analytics, and AI/ML use cases • Executive ownership of Snowflake environments, security model, and cost management • Define best practices for ingestion pipelines, schema design, RBAC, and performance • Partner with Security and IT on privacy, compliance, and data protection • Lead a small, high-impact Data Platform team (6–8 roles) • Oversee ingestion of enterprise systems including registration, eCommerce, finance, inventory, and CRM • Ensure reliable, testable, and well-documented data pipelines • Establish development standards, CI/CD, and operational support models • Own the data and analytics foundation for core Enterprise KPIs • Partner with business leaders to define, standardize, and certify enterprise metrics • Establish authoritative data models and semantic definitions in Snowflake • Ensure executive dashboards consume certified, governed datasets • Oversee data quality, reconciliation, lineage, and metric integrity • Enable scalable self-service analytics with strong governance guardrails • Act as the single point of accountability for enterprise KPI data integrity • Align platform priorities to executive reporting and strategic initiatives • Present platform readiness, risks, and KPI trust indicators to leadership • Partner with BI and Finance teams to evolve KPI definitions over time