• Own end-to-end data migration execution for enterprise and multi-location customers from discovery through cutover and validation • Lead client-facing data discovery and data mapping sessions to define source structures, transformation rules, and migration scope • Profile, cleanse, normalize, deduplicate, and transform large datasets using Python and Pandas • Write advanced SQL queries (MySQL and PostgreSQL) to validate, transform, and reconcile migrated data • Define and apply business rules for matching, merging, and record standardization • Build repeatable migration scripts and reusable data transformation workflows using version control best practices • Partner with Implementation, Solutions Engineering, Product, and Support to resolve data-related blockers and edge cases • Create clear technical documentation including data mapping specs, transformation logic, validation plans, and migration runbooks • Design and execute data validation and integrity checks before and after migration • Identify risks early and define rollback or remediation approaches when needed • Contribute to continuous improvement of MoeGo’s migration tooling, standards, and playbooks

• Design and own complex, enterprise-scale data architectures across MS Fabric, Azure, GCP, AWS, or Databricks serverless or hosted environments. • Define and enforce architectural standards, patterns, and governance frameworks across ingestion, modeling, lineage, security, and orchestration. • Shape AI-enabled architecture approaches, including data foundations for ML, feature engineering, and low-latency operationalization pipelines. • Act as a principal advisor to client technical leadership, helping shape long-term strategy, roadmaps, and modernization initiatives. • Lead architectural direction during pre-sales cycles, including solutioning, scoping, estimation, and executive-level presentations. • Anticipate downstream impacts of architectural decisions; maintain ownership when delivery teams or constraints require deviation from the original design. • Architect highly available, distributed, fault-tolerant data pipelines supporting batch and streaming workloads. • Oversee migration and integration of complex, diverse data sources into Fabric, Azure, GCP, or Databricks platforms. • Define medallion/lakehouse modeling patterns across Bronze/Silver/Gold zones or cloud equivalents. • Set enterprise standards for ingestion → transformation → serving layers across multi-cloud environments. • Optimize performance of large-scale data processing across Spark, Databricks, and Fabric-native engines. • Provide leadership across 2–3 concurrent projects with variable allocation, ensuring architectural consistency and delivery quality while also contributing to in-depth technical work where needed.

• 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