Data Engineering Modernization Architect

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 strong 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.
  

• 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.
  

• 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.