We’re Hiring: Data Lakehouse Engineer / Data Platform Specialist

Job Code: WS-090

Job Title: Data Lakehouse

Experience: 7 to 10 years

Location: India

Employment Type: Contract

Role Overview

We are looking for an experienced Data Lakehouse professional to design, implement, manage, and optimize an enterprise-scale Data Lakehouse platform. The ideal candidate will have strong hands-on expertise in Microsoft Fabric, Azure Data Lake, SQL, Python, PySpark, Spark SQL, Delta Lake, and modern cloud data engineering.

The role involves building scalable data pipelines, implementing medallion architecture, ensuring data quality and governance, and delivering trusted, reusable data products to support business intelligence, advanced analytics, and AI/ML initiatives.

Experience with enterprise data integration, real-time data processing, and automotive, manufacturing, engineering, or connected-vehicle data will be an advantage.

Key Responsibilities

- Design, implement, operate, and continuously improve the enterprise Data Lakehouse platform.
- Develop scalable data architectures combining data-lake flexibility with governed data-warehouse capabilities.
- Implement medallion architecture with raw (Bronze), validated (Silver), and curated (Gold) data layers.
- Design and maintain data ingestion pipelines for structured, semi-structured, and unstructured data across batch and streaming workloads.
- Build and manage data pipelines integrating SAP, manufacturing, engineering, finance, HR, commercial, customer, and connected-vehicle systems.
- Develop and optimize data transformations, orchestration workflows, and scheduling processes.
- Implement data-quality controls covering completeness, accuracy, consistency, validity, uniqueness, and timeliness.
- Maintain technical and business metadata, data dictionaries, schemas, dataset descriptions, and data lineage.
- Deliver trusted, curated datasets for Power BI, SAP Analytics Cloud, advanced analytics, AI, and machine-learning applications.
- Implement CI/CD, Git-based version control, Infrastructure-as-Code, and automated deployment practices for data platforms.
- Monitor pipeline execution, data freshness, storage utilization, processing performance, compute consumption, and platform capacity.
- Troubleshoot data pipeline failures, perform root-cause analysis, and improve platform reliability.
- Optimize data partitioning, schema evolution, query performance, storage,



and processing workloads.
- Implement data governance, access controls, data classification, retention, encryption, and data-protection measures.
- Support automotive and EV analytics across product development, manufacturing, quality, supply chain, charging, sales, and aftersales.
- Manage Microsoft Fabric workspaces, tenant governance, capacity utilization, and platform administration where applicable.
- Collaborate with Data Governance, Analytics, AI, Integration, business teams, external suppliers, and implementation partners.
- Continuously improve data availability, reliability, reuse, governance, and business value.

Required Skills & Competencies
- Robust experience in Data Lakehouse architecture and modern cloud data platforms.
- Hands-on expertise in Microsoft Fabric, Azure Data Lake, or equivalent cloud data-engineering platforms.
- Proficiency in SQL, Python, PySpark, and Spark SQL.
- Knowledge of Delta Lake, Apache Spark, Parquet, and open data-storage formats.
- Experience developing batch and streaming data pipelines.
- Strong understanding of medallion architecture and data-lakehouse design principles.
- Experience handling structured, semi-structured, and unstructured data.
- Expertise in data ingestion, transformation, orchestration, and workflow scheduling.
- Strong knowledge of relational, dimensional, and analytical data modelling.
- Experience with partitioning, schema evolution, query optimization, and workload tuning.
- Familiarity with Microsoft Fabric components, including OneLake, shortcuts, notebooks, pipelines, and SQL analytical endpoints.
- Experience integrating enterprise systems such as SAP, databases, APIs, files, manufacturing systems, and cloud applications.
- Understanding of API-driven, event-driven, and real-time data architectures.
- Strong data profiling, cleansing, validation, reconciliation, and quality-management skills.
- Knowledge of metadata management, data catalogs, business glossaries, and end-to-end data lineage.




- Understanding of enterprise data governance, data ownership, stewardship, classification, and retention.
- Experience with role-based access control, encryption, masking, and data-protection practices.
- Knowledge of CI/CD, Git, Infrastructure-as-Code, release management, and environment governance.
- Experience with platform monitoring, data observability, alerting, service-level monitoring, and root-cause analysis.
- Understanding of cloud cost optimization, capacity planning, and FinOps principles.
- Ability to develop reusable data products and translate analytics and AI requirements into reliable data solutions.

Education & Certifications
- Bachelor’s degree in Data Engineering, Data Science, Computer Science, Software Engineering, Information Technology, or a related discipline.
- Microsoft Fabric Data Engineer, Azure Data Engineer, or equivalent current certification is preferred.
- Microsoft Fabric Analytics Engineer certification is advantageous.
- Certifications or formal training in Apache Spark, Delta Lake, Databricks, or cloud data engineering are beneficial.
- Training in SQL, Python, data modelling, data governance, and metadata management is preferred.
- Cloud architecture, DevOps, cybersecurity, CI/CD, and Infrastructure-as-Code certifications are advantageous.
- DAMA, DCAM, or equivalent data-management certification is a plus.
- Training in data observability, data-quality management, API integration, event-driven architecture, or real-time data processing is beneficial.

Preferred Qualifications
- Experience building and governing enterprise-scale data platforms.
- Exposure to Microsoft Fabric workspace administration, tenant governance, and capacity management.
- Experience delivering reusable data products for business intelligence, analytics, and AI/ML workloads.
- Familiarity with automotive, EV, manufacturing, engineering, or connected-vehicle data domains.
- Strong troubleshooting, stakeholder-management, collaboration, and continuous-improvement skills.

Interested candidates: Please share your updated resume highlighting your experience in Microsoft Fabric, Azure Data Lake, PySpark, SQL, data pipeline development, and enterprise Data Lakehouse implementation. #Hiring #DataLakehouse #DataEngineering #MicrosoftFabric #AzureDataLake #PySpark #DataArchitecture #DeltaLake #CloudDataEngineering #DataGovernance

📌 Data Lakehouse Engineer / Data Platform Specialist (India)
🏢 Philodesign Technologies
📍 India