• Lead end-to-end delivery of enterprise data migrations from corporate systems (SAP, Oracle, Epic ERP) to target cloud data platforms, including the design of cloud landing zones, data governance frameworks, and system rationalization strategies. Establish migration compliance controls, automated rollback procedures, and operational readiness gates while owning full technical accountability for 12–18+ month migration roadmaps. • Build production-grade data connectors to SAP (RFC, IDoc, BAPI, OData), Oracle (AQ, GoldenGate, APIs), and SQL/non-relational sources. Develop ETL/ELT pipelines with LLM-enabled transformation logic, multi-layer validation and reconciliation frameworks, and optimized throughput for datasets scaling from tens of millions to billions of records with built-in CDC and incremental loading. • Construct semantic ontology layers translating raw ERP structures into business-consumable objects (Customer, Order, Invoice, Product, Vendor, Asset). Deploy automated schema mapping agents for source-to-target analysis and transformation logic generation. Build unified master data models with row/column-level security, cross-system lineage tracking, and AI-ready semantic structures. • Build operational dashboards, migration control centers, and agent-driven workflows for automated validation, exception handling, and anomaly detection using low-code platform tools. Generate TypeScript/Python SDKs for custom integrations and deliver real-time monitoring and self-service interfaces for migration progress, data quality KPIs, and compliance tracking. • Lead consolidation of 5–15+ fragmented ERP instances into standardized master data models. Resolve complex entity resolution challenges including customer matching, product harmonization, and chart of accounts unification. Establish golden record frameworks, data quality scorecards, survivorship rules, and data stewardship workflows for post-migration governance. • Serve as primary technical advisor to C-suite and enterprise architecture stakeholders across all engagement phases. Deploy discovery agents to analyze legacy data estates, conduct assessment workshops, facilitate solution design sessions, and deliver executive briefings, go/no-go readiness assessments, and prioritized modernization roadmaps. • Build reusable migration accelerators, playbooks, and reference architectures that scale across engagements. Lead knowledge transfer to upskill client teams for post-migration ownership and collaborate with internal product and sales engineering teams to feed field insights back into platform development and delivery methodology. • Operate autonomously in ambiguous, high-stakes client environments, driving outcomes with minimal oversight; translate deeply technical concepts into clear, business-level narratives for C-suite audiences through executive briefings and stakeholder communications; navigate organizational complexity, competing stakeholder priorities, and enterprise change management dynamics to maintain momentum across multi-workstream engagements; mentor junior engineers, cultivate technical capability within delivery teams, and foster a culture of knowledge sharing and continuous improvement.

Senior Data Engineer Job Summary The Senior Data Engineer works collaboratively within their team to perform feature analysis, research, requirements gathering, defining product architecture, designing features, implementation/coding, testing, deployment, maintenance, and support of RedSail products. The Senior Data Engineer is a vital member of a Scrum team actively participating in all aspects of the Scrum workflow. The focus of a Senior Data Engineer will be on database design and architecture, ELT/ELT, data lake/warehouses, data security, development, troubleshooting, optimization, design/code reviews, product quality, working within the team, mentoring others, and assisting with product directions.

• Act as the technical bridge between a signed contract and a successful launch • Map out customer technical onboarding journeys • Own the end-to-end data migration process • Provide integration support using API and system integrations • Translate customer problems into technical requirements

• Build, maintain, and optimize scalable data pipelines and workspaces within the Databricks environment. • Develop and audit complex reporting models focused on advertising performance metrics and financial billing. • Write high-performance SQL queries and leverage Jinja SQL to create dynamic and reusable transformations. • Design data models that support accurate and actionable business insights. • Collaborate with cross-functional teams to ensure data reliability and reporting accuracy. • Explore and implement basic AI/ML enhancements such as predictive billing models or anomaly detection. • Support automation efforts through CI/CD practices using GitHub and Jenkins. • Maintain clean and well-documented configuration files using YAML/YML. • Continuously improve data workflows for efficiency, scalability, and quality.