• 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

Role Description We are seeking an experienced SAP Data Migration Engineer to design, execute, and support large-scale data migration initiatives within SAP S/4HANA environments. This role focuses on the full data migration lifecycle — extraction, transformation, validation, reconciliation, and cutover — to ensure accurate, complete, and compliant migration of enterprise data from legacy systems into SAP. The ideal candidate will combine strong hands-on experience with SAP LTMC, SLT, LVM, and S/4HANA migration tools with deep understanding of SAP data structures, business processes, and data governance, and will partner closely with functional teams, business owners, and infrastructure stakeholders during all phases of migration. Qualifications - Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. - Five or more years of SAP data migration experience. - Strong hands-on experience with SAP LTMC and S/4HANA migration tools. - Experience with SAP SLT replication scenarios. - Working knowledge of SAP LVM for system refresh and landscape management. - Strong understanding of SAP master and transactional data structures. - Experience performing data validation and reconciliation processes. - Strong SQL and data analysis skills. - Experience working in enterprise SAP landscapes during major implementations. - Strong troubleshooting and documentation skills. Requirements - Design and execute SAP data migration strategies for S/4HANA implementations, including conversion and greenfield scenarios. - Perform data extraction from legacy systems using SAP-provided and custom tools and prepare data for ingestion into SAP. - Configure and utilize SAP LTMC (Legacy Transfer Migration Cockpit), including custom migration objects where required. - Implement real-time replication scenarios using SAP SLT (System Landscape Transformation), including transformation rules and filter conditions. - Manage logical system setups and replication configurations for hybrid SAP environments. - Support system refreshes and landscape virtualization activities using SAP LVM (Landscape Virtualization Management). - Design and execute data transformation, cleansing, and enrichment processes prior to migration. - Develop comprehensive validation and reconciliation reports to ensure data accuracy and completeness. - Identify and resolve data inconsistencies, duplicates, and orphan records prior to go-live. - Support mock migrations, dress rehearsals, and the final cutover, including detailed runbook execution and rollback planning. - Optimize performance for large-volume data loads, including parallelization, package sizing, and database tuning. - Collaborate with functional teams to validate master and transactional data against business expectations. - Ensure compliance with enterprise data governance, audit, and privacy standards. - Maintain comprehensive, current technical documentation — including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures. Benefits - Competitive base salary commensurate with experience, plus benefits. - 100% remote, full-time, direct W2 position. - Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap.