• Architect & Scale Modern Data Infrastructure – Design, build, and optimize scalable data lakes, warehouses, and data pipelines using Snowflake and modern cloud platforms (AWS or Azure) to support enterprise reporting, analytics, and advanced use cases • Own Data Modeling & Transformation Layers – Develop and maintain robust data models (ELT/ETL), ensuring clean, reliable, and well-documented datasets that serve as the foundation for business intelligence and operational reporting • Build & Maintain Scalable Data Pipelines – Design and manage end-to-end data pipelines that ingest, transform, and unify data from multiple systems into a centralized, high-performance data environment • Integrate Disparate Source Systems – Lead the integration of fragmented operational, financial, and HR systems into a cohesive architecture that enables reliable cross-system reporting and insights • Translate Business Needs into Technical Architecture – Partner with stakeholders to understand current and future business requirements, translating them into scalable system design, data standards, and architectural decisions • Ensure Data Quality, Governance & Performance – Establish standards for data reliability, security, scalability, and performance as the platform grows through acquisition and expansion • Navigate Ambiguity with Curiosity – Ask thoughtful questions, explore data proactively, and bring structure to evolving requirements in a fast-paced, high-growth environment • Data Visualization & Reporting – Design and deliver dashboards using Power BI, Looker, or Sigma, ensuring insights are accessible, actionable, and aligned with leadership priorities

• Own the end-to-end architecture and delivery of ENFRA’s Modern Data and AI platform • Translate enterprise data and AI strategy into a production-ready, scalable, and secure platform supporting analytics, reporting, and AI agents • Define and evolve platform reference architecture across ingestion, landing, standardization, curation into Snowflake marts, semantic layers, AI integration, and consumption patterns • Partner with application teams to ensure reliable, observable pipelines with defined SLAs/SLOs, lineage, and quality checks • Architect platform-level capabilities supporting applied AI, generative AI, and agent-enabled workflows

• Design and implement **scalable streaming data platforms** to support real-time ingestion, processing, and analytics • Architect and guide the development of **end-to-end data platforms** across batch and streaming workloads • Lead and contribute to **Master Data Management (MDM)** solutions, including: • Golden record design • Data matching, survivorship, and hierarchy management • Integration patterns with downstream consumers • Define and implement **data governance frameworks**, including: • Data ownership and stewardship models • Data quality rules and monitoring • Metadata, lineage, and access controls • Collaborate with application teams to expose data via **APIs and event-driven architectures** • Provide architectural guidance for **cloud-native deployments**, including containerization and orchestration • Establish **data architecture standards, patterns, and best practices** • Partner with DevOps teams to enable CI/CD, infrastructure automation, and platform reliability • Review designs, mentor engineers, and help drive technical decisions across projects

• Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals). • Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows. • Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve. • Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs). • Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost. • Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines. • Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues. • Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.