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Senior Data Engineer

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

  • 4+ years of professional data engineering or analytics engineering experience.

  • Strong expertise in:

    • Dimensional modeling (star & snowflake schemas)

    • Data Vault 2.0 (Hubs, Links, Satellites, business keys, historization)

    • Understanding transitions between Data Vault, dimensional and reporting models.

  • Hands-on experience in enterprise-scale data migration projects, including:

    • multi-source ingestion & harmonization

    • schema evolution and refactoring

    • managing history and auditability

  • Advanced SQL skills for:

    • complex transformations

    • historization logic

    • reconciliation & validation

  • Experience with modern data warehouses / lakehouse platforms.

  • Strong understanding of:

    • data quality

    • lineage

    • governance

    • traceability

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.

  • Understanding of observability for data workflows (validation metrics, regression detection, quality thresholds).

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

    • AI accelerator development teams

  • Ensure AI-assisted workflows remain grounded in correct modeling principles.


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).



Full time
Jaipur
2026-02-12
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