Forward Deployed Engineer – Data Migration, Data Consolidation Platforms

Role Overview

• 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.

Requirements

• 7-10+ years of progressive experience in enterprise data engineering, data migration, or large-scale system integration roles within complex, multi-platform environments
• 3-5+ years directly leading end-to-end data migration or multi-system consolidation programs for Global Enterprises and Industry Leaders, with full ownership of technical delivery and client outcomes
• Demonstrated client-facing experience serving as a trusted technical advisor to C-level executives, enterprise architecture teams, and cross-functional business stakeholders
• Proven industry depth in at least two of the following verticals: Healthcare, Financial Services, Manufacturing, Retail, Energy & Utilities, or Public Sector
• Hands-on migration complexity: successfully delivered programs involving at least 3+ heterogeneous source systems, 100M+ records, complex master data harmonization, and multi-phase cutover execution
• Advanced proficiency in Python and SQL with working experience in PySpark and TypeScript/JavaScript
• Hands-on expertise with modern ETL/ELT and data integration platforms (Informatica, Talend, Matillion, Fivetran, AWS Glue, Azure Data Factory)
• Proven ability to build scalable, version-controlled data pipelines with error handling, incremental loading, and Change Data Capture (CDC)
• Strong working knowledge of at least one major cloud provider (AWS, Azure, or GCP), including core infrastructure, managed data services, and security configurations
• Experience with enterprise data warehouse and lakehouse platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse Analytics, Delta Lake)
• Familiarity with knowledge graph construction, semantic modeling, ontology frameworks (RDF, OWL), or platforms such as Neo4j, AI Foundry, or Stardog
• Practical experience integrating LLMs or AI-driven tooling into data transformation, schema inference, or mapping workflows (OpenAI, Anthropic, AWS Bedrock)
• Experience with low-code/no-code application platforms for rapid solution delivery (AI Foundry, Mendix, OutSystems, PowerApps)

Tech Stack

• Amazon Redshift
• AWS
• Azure
• BigQuery
• Cloud
• ERP
• ETL
• Google Cloud Platform
• Informatica
• JavaScript
• Matillion
• Neo4j
• Oracle
• PySpark
• Python
• SQL
• TypeScript