• Own data pipelines and infrastructure: Design, implement, and evolve scalable, production-grade systems using tools such as Dagster, Airflow, Snowflake, AWS, MongoDB, and dbt. Apply a cloud-native and DevOps mindset using CI/CD, infrastructure-as-code, monitoring, and automated testing to build reliable systems. Partner with cross-functional teams to deliver solutions that meet both immediate product needs and long-term organizational strategy. • Lead data ingestion and integration: Bring in complex, high-volume datasets while ensuring strong data contracts, freshness, quality, integrity, and lineage, and build systems that empower domain experts to contribute to and maintain their own data pipelines. • Transform raw data into trusted data products: Convert raw inputs into structured, usable datasets that empower our analytical and product teams. Collaborate closely with operational data managers to ensure data models and intuitive, reliable alignment with how data is consumed in practice. • Leverage AI: Make informed judgement calls about how AI can be a force-multiplier for both your own work and the team’s and how it can’t. • Elevate the team: Mentor engineers, actively shape technical direction through architectural reviews and roadmap planning, and build team culture through documentation and knowledge sharing.

• 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