Senior Data Engineer (Microsoft Fabric and Databricks)

The Senior Data Engineer is responsible for the design, development, administration, governance, and operational support of enterprise data platforms built on Microsoft Fabric. This role combines deep data engineering expertise with platform administration capabilities to ensure scalable, secure, reliable, and cost-effective data solutions that support business operations, analytics, AI initiatives, and project delivery. The position requires 10-15 years of overall relevant experience in data engineering, data platforms, cloud technologies, and enterprise data management. The role will design, build, and support data ingestion, ETL/ELT pipelines, lakehouse architectures, and analytical datasets using Microsoft Fabric, Databricks, Python, PySpark, SQL, Spark, Delta Lake, OneLake, and Data Pipelines. In addition, the role will govern Databricks and Fabric environments, including security, access management, monitoring, performance tuning, capacity management, and platform operations. The Senior Data Engineer will collaborate with business stakeholders, architects, and analytics teams to deliver robust data solutions while ensuring platform reliability, compliance, security, and operational excellence. This role will also drive continuous improvement in data quality, governance, automation, performance, and cost optimization.

Major Responsibilities Data Engineering & Solution Delivery

• Own end-to-end technical design, development, and delivery of enterprise data engineering solutions.
• Design and implement scalable ETL/ELT, data integration, and data transformation pipelines for structured and unstructured data sources.
• Develop and maintain batch, near real-time, and streaming data ingestion pipelines from databases, APIs, files, cloud storage, and enterprise applications.
• Build reusable notebooks, data transformations, and processing frameworks using Databricks, Microsoft Fabric, Python, PySpark, and SQL.
• Design and implement Lakehouse architectures using Delta Lake, OneLake, and medallion architecture (Bronze, Silver, Gold layers).
• Create trusted datasets, curated data products, and semantic-ready data structures supporting analytics, reporting, AI, and machine learning initiatives.
• Implement data quality frameworks, validation rules, reconciliation controls, and exception management processes.
• Design scalable data models, dimensional schemas, and data marts to optimize reporting and analytical workloads.
• Optimize Spark workloads, SQL queries, storage strategies, partitioning, and data processing performance.

Databricks & Microsoft Fabric Administration

• Administer Databricks workspaces, Fabric capacities, Fabric workspaces, Lakehouses, Data Warehouses, Dataflows, and Pipelines.
• Configure and maintain user access, security groups, RBAC permissions, service principals, and identity integrations.
• Manage secrets, credentials, key vault integrations, and secure access to enterprise data sources.
• Monitor platform health, resource utilization, workload performance, and capacity consumption.
• Perform platform upgrades, feature enablement, release validation, and environment maintenance activities.
• Implement capacity planning, workload management, and resource allocation strategies.
• Support governance initiatives by enforcing workspace standards, naming conventions, tagging, and compliance controls.

Operations, Governance & Reliability

• Develop operational runbooks, support procedures, and disaster recovery documentation.
• Monitor data pipelines and platform services to ensure availability and reliability.
• Establish and maintain metadata management, data lineage, cataloging, and governance processes.
• Implement observability, logging, monitoring, and alerting across the data platform ecosystem.

• Drive cost optimization initiatives across compute, storage, Fabric capacities, and Databricks workloads.

• Establish Git-based source control and branching strategies for notebooks, pipelines, and infrastructure components.
• Implement CI/CD pipelines for automated deployment, testing, environment promotion, and release management.

• Develop reusable deployment templates and engineering accelerators for platform consistency and scalability.

• Communicate technical solutions, trade-offs, risks, dependencies, and delivery plans to both technical and non-technical stakeholders.

• B.Tech in Computer Science, Information Technology, Engineering, or an equivalent degree is required.
• 10-15 years of overall relevant experience in data engineering, data platforms, analytics, and cloud technologies.
• Minimum 5+ years of hands-on experience with Databricks and 2+ years Microsoft Fabric environments.
• Proven experience designing and supporting production-grade ETL/ELT and data integration solutions.
• Experience administering enterprise-scale Databricks and Microsoft Fabric platforms.
• Strong experience working with Azure data services and cloud-native architectures.
• Demonstrated experience in stakeholder engagement, solution ownership, and technical leadership.
• Experience mentoring engineers and supporting enterprise platform operations is preferred.

Required Knowledge and Skills Data Engineering

• Strong expertise in Databricks, Spark, Delta Lake, Microsoft Fabric, OneLake, Lakehouse, and Data Warehouse architectures.
• Advanced proficiency in Python, PySpark, SQL, and Notebook-based development.
• Expertise in ETL/ELT frameworks, data ingestion patterns, incremental processing, CDC, and data transformation techniques.
• Strong understanding of dimensional modeling, data warehousing, and semantic data structures.
• Experience with batch processing, streaming architectures, and real-time data integration.

Platform Administration

• Strong knowledge of Databricks Workspace Administration and Microsoft Fabric Administration.
• Experience managing capacities, workspaces, governance controls, security policies, and platform operations.
• Expertise in Microsoft Entra ID (Azure AD), RBAC, access controls, service principals, and security integration.
• Experience with secrets management, key vaults, credential management, and secure data access.
• Knowledge of platform monitoring, observability, troubleshooting, incident management, and operational excellence.

Governance & DevOps

• Strong understanding of data governance, metadata management, lineage, data cataloging, and compliance controls.
• Experience with Git, Azure DevOps, CI/CD pipelines, release management, and automated testing.
• Knowledge of Infrastructure as Code, automation frameworks, and deployment standards.
• Experience implementing enterprise data quality and reliability frameworks.

Performance & Cost Optimization

• Experience tuning Spark applications, SQL workloads, storage structures, and distributed compute environments.
• Knowledge of Fabric capacity optimization, Databricks cluster optimization, workload management, and FinOps practices.

• Ability to balance performance, scalability, reliability, security, and cost considerations.

• Ability to work effectively in a fast-paced enterprise environment supporting multiple business priorities., * Databricks Certified Data Engineer Professional
• Databricks Certified Data Engineer Associate