We are seeking a Senior Data Engineer to design and implement reusable accelerators that improve the speed, quality and reliability of large-scale data migration and modernization programs.

This role focuses on deep data-engineering expertise — modeling, transformation, migration strategy, and validation — while collaborating closely with AI engineers to embed automation and intelligence into migration workflows.

The ideal candidate brings strong hands-on data modeling and migration experience, understands enterprise data platforms and can guide teams on best practices across ingestion, core modeling and consumption layers.

Roles & Responsibilities

• Design and implement reusable data accelerators that support migration and modernization initiatives.

• Lead schema interpretation, data-model translation, and transformation logic design across legacy and modern systems.

• Define and implement migration validation, reconciliation, and quality-check frameworks.

• source analysis

• metadata interpretation

• transformation generation

• reconciliation workflows

• Build and maintain structured context pipelines grounded in:

• schemas

• SQL logic

• Data Vault models

• metadata and lineage artifacts

• Guide junior engineers on modeling tradeoffs across staging, core and reporting layers.

• Establish reusable standards for:

• data-model documentation

• migration traceability

• validation & auditability

• Act as a senior data advisor during accelerator adoption on live client engagements.

• Contribute to reference architectures, design artifacts and reusable migration patterns.

Core Data Engineering & Modeling Skills

AI & Automation (Good to Have)

AI experience is not mandatory, but familiarity with AI-enabled tooling is a plus.

Preferred exposure includes:

• Working with LLM-enabled tools over structured data
• Metadata-driven automation approaches
• Retrieval over schemas, SQL and metadata
• RAG-style patterns for structured data
• Evaluating AI-generated SQL or transformation logic

The expectation is to collaborate effectively with AI engineers — not necessarily build AI systems independently.

Engineering & Platform Expectations

• Proficiency in Python for data tooling and automation.

• Experience integrating with:

• data warehouses / lakehouses

• ETL / ELT pipelines

• metadata & catalog systems

• Familiarity with version-controlled and CI/CD-driven data environments.

Leadership & Collaboration

• Serve as a senior technical advisor for migration-focused accelerator initiatives.

• Mentor engineers on modeling best practices and migration patterns.

• Help delivery teams reason about data architecture trade-offs.

• Act as the bridge between:

• Data engineering delivery teams

Requirements Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field with 2+ years of IT experience.
• 4+ years of experience in data engineering, analytics engineering or data modelling.
• Demonstrated experience in enterprise-scale data migration.
• Strong SQL and modeling expertise (Data Vault / Dimensional).
• Experience working in structured, collaborative engineering environments.
• Consulting or reusable-asset development experience is a plus.

Signs You May Be a Great Fit

• Strong ownership and architectural thinking in data projects.
• Ability to balance modeling rigor with delivery pragmatism.
• Comfortable working close to schemas, business logic and transformation complexity.
• Interested in evolving toward AI-assisted data engineering (without being an AI specialist).