[Remote] Azure Data Engineer (W2)
Note: The job is a remote job and is open to candidates in USA. Nasscomm is seeking an Azure Data Engineer to work directly with client stakeholders on critical data engineering and migration initiatives. The role focuses on ingesting, modeling, validating, securing, and organizing enterprise data from legacy SAP systems into a modern Azure data lake environment.
Responsibilities
- Data Ingestion & Pipelines: Ingest data from legacy SAP platforms into the Azure Data Lake and target platforms using Azure Synapse and Azure Data Factory (ADF)
- Data Modeling & Architecture: Ingest, model, and structure all in-scope datasets (including star schemas, SCD patterns, metadata, lineage, and documented business rules) to support performance, scalability, retention, and query requirements
- Data Integrity & Quality: Perform data validation, reconciliation, acceptance testing, and basic quality checks to ensure migrated data is complete, accurate, consistent, and audit-ready
- Security & Compliance: Maintain robust security controls and compliance obligations throughout the migration process, ensuring protection of sensitive data and enforcing role-based access
- Project Execution & Governance: Deliver technical activities within agreed timelines and effort limits, surfacing risks and issues early through defined governance processes
Skills
- • Experience: 2–5 years of hands-on experience in data engineering or closely related data development roles
- • Azure Suite (Required - Intermediate, 3–5 years): Deep hands-on experience building/supporting pipelines in Azure Data Factory (ADF) and transformations in Azure Synapse Analytics (Synapse workspace, dedicated/serverless SQL pools, integration pipelines, Synapse Studio)
- • SQL & Data Modeling (Required - Intermediate, 3–5 years): Strong proficiency in SQL and practical experience implementing standard data models (star schemas, Slowly Changing Dimensions)
- • Data Quality (Required - Intermediate, 3–5 years): Hands-on experience with data quality validation, troubleshooting pipeline failures, and collaborating with senior engineers for root-cause analysis
- • Platform Knowledge: Strong understanding of both source (legacy/SAP) and target (Azure) data architectures
- • SAP Familiarity (Preferred): Familiarity with SAP data (Supply Chain, Operations) and SAP BW concepts (InfoCubes, DSOs, CompositeProviders, BW Queries, Open Hub, ODP). Ability to interpret SAP-sourced fields/keys and work with functional partners
- • ETL & Connectivity (Preferred): Familiarity with SAP Data Services or third-party SAP connectors (e.g., Theobald Xtract, SDI)
- • Python / ML (Preferred - Intermediate, 3–5 years): Experience using Python for data manipulation, scripting, or basic machine learning workflows
Benefits
- Remote work arrangement
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