Data Engineering Modernization Architect

12+ years of overall experience in data engineering, data platforms, analytics, or enterprise data architecture, with 5+ years in data platform modernization/cloud data engineering and solid hands-on expertise in Databricks, Microsoft Fabric and Azure Data Factory.

Role
Overview

- We are looking for an
experienced Data Engineering Modernization Architect to lead the
transformation of legacy data platforms, ETL pipelines, and data
warehouses into modern, scalable, cloud-native data platforms.

- The architect will define the target-state
data architecture, modernization strategy, migration roadmap, engineering
standards, and reusable frameworks. The role requires a strong combination
of architecture, hands-on engineering, cloud technologies, data modeling,
migration, performance optimization, and automation.

- The ideal candidate should have
successfully delivered at least two enterprise-scale data engineering
modernization projects, involving migration from legacy ETL/data warehouse
technologies to platforms such as Databricks and Microsoft Fabric, One
Lake with Azure Data Factory or equivalent orchestration.

Key Responsibilities

- Lead modernization of legacy
data platforms, ETL/ELT processes and data warehouses into modern
cloud-based architectures. Assess existing data platforms, pipelines,
databases, ETL jobs, data models and workloads. Define As-Is, To-Be and
transition architectures, Develop modernization roadmaps, Identify
opportunities to simplify and consolidate legacy data pipelines, Define
reusable modernization patterns and frameworks.

Key Skills Requirement

- Strong hands-on and




architectural expertise in Databricks Lakehouse architecture, PySpark,
Delta Lake, Delta Live Tables / Lakeflow capabilities, Unity Catalog,
Workflows / Jobs, Databricks SQL, Change Data Capture, Schema evolution,
Databricks security and governance, Azure Data Factory ADF pipelines, Copy
activities, Mapping Data Flows, Parameters and variables, Metadata-driven
pipelines, Dynamic pipelines, Integration Runtime, Self-hosted Integration
Runtime, Trigger mechanisms, Error handling, Retry frameworks, Dependency
management, Pipeline monitoring, CI/CD

- The candidate should be strong
in Enterprise Data Architecture, Modern Data Lakehouse, Data Warehouse,
Data Lake, Lakehouse architecture, Medallion architecture, Dimensional
modelling, Star/Snowflake schemas, Data Vault, Data mesh concepts, Batch
and streaming architecture, CDC architecture, Event-driven data pipelines,
Master/reference data, Data governance, Data lineage, Data quality,
Metadata management

- Strong experience in developing
reusable frameworks for Metadata-driven ingestion, Dynamic pipeline
generation, Automated schema discovery, Automated table creation,
Incremental load framework, CDC framework, Data quality framework,
Pipeline monitoring, Exception handling, Automated reconciliation, Data
validation, Deployment automation, Environment provisioning

- Strong experience in Legacy ETL
modernization, Data warehouse migration, Data lake migration, ETL → ELT
transformation, On-premises → Azure migration, Legacy database → Lakehouse
migration, Batch → near-real-time modernization, Stored procedure
modernization, Legacy ETL code conversion.

📌 Data Engineering Modernization Architect (Chennai)
🏢 Important Business
📍 Chennai