Z2_Azure Data Engineer

nttdata

Bangalore 10 Years Exp Posted 1h ago

Job Description

BI FSL 2.0 Transformation & Solution Architecture

  • Lead the end-to-end architecture and execution of BI FSL 2.0 transformation from SAP BW/BW4HANA to neXtollo/Azure Databricks.
  • Translate existing SAP BW data models, transformations, process chains, extractors, and reporting logic into scalable Databricks Lakehouse patterns.
  • Define the target-state data architecture covering ingestion, bronze/silver/gold layers, semantic consumption, Power BI integration, and reusable data products.
  • Design SAP-to-Azure extraction and integration patterns using relevant approaches such as ODP, SLT, BW Bridge, APIs, CDC, or custom ETL frameworks.
  • Drive fit-gap analysis between existing SAP BW capabilities and future-state neXtollo architecture.
  • Establish architecture standards, design principles, reusable engineering patterns, and governance guidelines for the transformation program.
  • Collaborate with business stakeholders, SAP functional teams, platform teams, data engineers, Power BI teams, and program leadership to ensure aligned execution.

Azure Data Engineering, DevOps & Platform Engineering

  • Design, build, and optimize enterprise-scale data pipelines using Azure Databricks, PySpark, Delta Lake, Azure Data Factory, and ADLS Gen2.
  • Implement CI/CD pipelines for notebooks, workflows, data pipelines, infrastructure, configuration, and deployment across development, test, and production environments.
  • Automate infrastructure provisioning and environment setup using Infrastructure as Code, preferably Terraform or equivalent Azure-native tooling.
  • Define release management, branching strategy, code review process, deployment gates, rollback approach, and environment promotion standards.
  • Implement observability, monitoring, alerting, logging, data quality checks, reconciliation, and operational dashboards for production-grade data platforms.
  • Embed DevSecOps practices including secure secret handling, service principals, managed identities, RBAC, Key Vault integration, policy compliance, and audit readiness.
  • Troubleshoot migration defects, pipeline failures, data mismatches, performance bottlenecks, and production issues with structured root cause analysis.

Leadership & Delivery Ownership

  • Provide technical leadership to data engineers, developers, analysts, and external partners throughout the migration lifecycle.
  • Own architecture decisions and ensure alignment with enterprise cloud, security, data governance, and platform standards.
  • Support roadmap planning, sprint execution, dependency management, technical risk mitigation, and delivery governance.
  • Create solution documents, architecture decision records, migration playbooks, runbooks, and design review material for leadership and engineering teams.
  • Act as a bridge between business outcomes and technical implementation, ensuring the platform enables reliable, faster, and future-ready analytics.

Required Technical Skills

Azure, Databricks & Lakehouse Architecture

  • Azure Databricks and Databricks Workflows
  • Delta Lake, Medallion Architecture, and Lakehouse design
  • Azure Data Factory or equivalent orchestration tools
  • Azure Data Lake Storage Gen2 and cloud object storage patterns
  • Azure SQL Database, Synapse Serverless SQL, or Databricks SQL
  • Unity Catalog, data lineage, access control, and data product governance
  • Azure Key Vault, Managed Identity, Service Principals, and secure connectivity
  • Power BI consumption patterns and semantic layer enablement

SAP BW/BW4HANA Migration & Integration

  • Strong understanding of SAP BW/BW4HANA concepts, data models, InfoProviders, transformations, process chains, and reporting dependencies.
  • Experience translating SAP BW logic into modern cloud data engineering patterns on Databricks.
  • Knowledge of SAP data extraction approaches such as ODP, SLT, CDS Views, RFC/API-based extraction, BW Bridge, or third-party replication tools.
  • Ability to assess migration complexity, data lineage, reconciliation needs, and functional equivalence between legacy and target platforms.
  • Exposure to SAP functional domains in logistics, supply chain, warehouse, transportation, or aftersales analytics is highly preferred.

Data Technologies

  • Python
  • PySpark
  • SQL/T-SQL
  • Spark
  • Delta Lake
  • Data Modeling
  • ETL/ELT Design
  • Data Warehousing Concepts
  • Performance Tuning

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