Data Engineer - Manager
Databricks Data Architect - Manager
Location: Bengaluru, India
Employment Type: Full-time
Reporting To: Jagadish Doki
Organization: KPMG Global Services (KGS)
About KPMG Global Services (KGS)
KPMG Global Services (KGS), established in India in 2008, is a strategic global delivery organization supporting over 50 KPMG member firms worldwide. With close to 10,000 professionals across Bengaluru, Gurugram, Kochi, and Pune, KGS delivers highimpact Advisory and Tax services through a scalable and customized delivery model. As part of KPMG in India, KGS is consistently recognized as a top employer for inclusion, diversity, and workplace culture.
Team Overview
KPMG's Data & Analytics practice helps clients unlock business value through trusted, highquality analytics solutions. The team takes a businessfirst approach to solving complex challenges across growth, risk, and performance. Professionals within this practice work at the intersection of data engineering, cloud platforms, and advanced analytics, enabling datadriven decisionmaking at scale.
Role Summary
The Databricks Data Architect - Manager will play a critical leadership role in designing, building, and scaling enterprisegrade data platforms using Databricks on Azure. This role is responsible for architecting robust data ingestion and transformation frameworks, enabling secure and governed data lakes, supporting cloud migration programs, and providing technical leadership across large, complex data engineering initiatives.
The role combines deep handson expertise, solution architecture, and stakeholder engagement, making it ideal for a senior data engineering leader who can translate business objectives into scalable technical solutions while mentoring teams and driving best practices
Key Responsibilities
- Architect, design, and deliver scalable data platforms using Azure Databricks, Apache Spark, and Delta Lake.
- Build and optimize endtoend data ingestion, processing, and transformation pipelines for large, complex datasets from multiple sources.
- Develop reusable and standardized frameworks to improve performance, reliability, and maintainability of data engineering workloads.
- Lead and support cloud migration initiatives, including modernization of legacy data platforms to Databricks and Azurenative services.
- Implement data security, governance, and privacy controls, ensuring compliance with enterprise and regulatory standards.
- Monitor, troubleshoot, and performancetune Databricks workloads with a focus on cost and compute efficiency.
- Collaborate closely with business stakeholders, analytics teams, and crossfunctional technology teams to ensure data quality and availability.
- Provide technical thought leadership, including defining architecture standards, patterns, and Best Practices for Databricks adoption.
- Deliver proofs of concept (PoCs) and solution demos to showcase Databricks capabilities to stakeholders and clients.
- Mentor and guide data engineers, promoting high engineering standards and continuous improvement.
Mandatory Skills & Experience
- Bachelor's or higher degree in Computer Science, Information Technology, or related discipline, with 9+ years of overall experience.
- Extensive handson experience with Databricks and Apache Spark, including PySpark, SQL, and Scala.
- Strong expertise in designing largescale data ingestion and transformation pipelines.
- Proven experience working with Azure services, including Azure Databricks, Azure Data Lake Storage, and Azure Blob Storage.
- Solid understanding of cloud architectures and data engineering migration methodologies.
- Experience implementing CI/CD and DevSecOps practices in Databricks environments.
- Knowledge of big data file formats such as Parquet and Avro, along with compression techniques.
- Strong communication skills with the ability to explain complex technical concepts to both technical and nontechnical audiences.
- Demonstrated ability to work independently, take ownership, and drive outcomes.
Preferred Qualifications
- Handson experience with Azure DevOps, Terraform, and Microsoft VSTS.
- Experience integrating Databricks with Azure Data Factory for orchestration.
- Familiarity with Power BI for analytics and reporting use cases.
- Understanding of Azure RBAC and IAM concepts for securing data platforms.
- Relevant certifications such as Databricks Certified Data Engineer or DP203: Data Engineering on Microsoft Azure.
- Exposure to AI/ML workloads and their integration with Databricks platforms.
Culture & Benefits
KPMG offers a collaborative, inclusive, and growthoriented environment with benefits that support personal and professional wellbeing, including parental leave, CSR initiatives, networking opportunities, and employee development programs. Benefits may vary by role and location.
15+ years of experience in data engineering, analytics, and technology leadership roles, with demonstrated success in delivering enterprise data solutions
Strong software/data engineering background, having started as an individual contributor and grown into senior leadership roles
Proven experience in leading large, distributed teams, managing organizational change, and scaling engineering functions
Expertise in building ETL/ELT pipelines, data warehouses, data marts, and analytics products on top of cloud-native platforms (GCP, AWS, or Azure)
Experience in SQL, Python, and one or more programming languages (Java, Scala, Go, etc.)
Deep understanding of data modeling, data computation frameworks, distributed systems, and big data architectures
Experience in working with frameworks and automation tools provided by shared services teams
Proficiency in Agile methodologies, DevOps/CI-CD principles, and data governance best practices
Strong collaboration skills with the ability to partner across engineering, product, and business functions
Excellent communication and stakeholder management skills; ability to represent the function internally and externally
Bachelor's, Master's, or PhD degree in Computer Science, Engineering, or a related field
Provide strategic and operational leadership for Data Engineering and Analytics teams to build data products, pipelines, and solutions that enable business intelligence, analytics, and AI/ML use cases.
Oversee the development and deployment of scalable ETL/ELT pipelines, data marts, and analytics models that meet enterprise data needs
Ensure effective use of the generic frameworks, tools, and platforms to build, test, and deploy data solutions efficiently
Partner closely with peers to align on capabilities, enhancements, and optimizations needed for ingestion, transformation, orchestration, and deployment frameworks
Drive a data product mindset, ensuring that data assets are well-architected, high quality, governed, and reusable across the organization
Foster collaboration with Data Product Management, AI/ML teams, and business stakeholders to prioritize and deliver high-impact data solutions
Build and scale a high-performing team through recruitment, coaching, and mentoring, promoting a culture of innovation, inclusion, and continuous learning
Proven experience in leading large, distributed teams, managing organizational change, and scaling engineering functions
Lead transformational initiatives to modernize data architecture, adopt emerging technologies, and evolve engineering practices
Cultivate a collaborative and transparent culture, breaking down silos and aligning cross-functional teams around shared goals
Define clear objectives, KPIs, and performance metrics for teams, and provide feedback to ensure accountability and growth
Focus on automation, efficiency, and quality, while meeting or exceeding delivery timelines
Ensure adherence to data governance, privacy, and security policies in all data engineering and analytics work
Represent Data Engineering and Analytics in cross-functional forums, advocating for best practices, innovation, and alignment with enterprise architecture
Preferred Skills
Experience delivering analytics self-service capabilities, conversational analytics and enterprise dashboards using BI tools (e.g., Looker, Power BI, Tableau)
Familiarity with AI/ML operationalization, including enabling data pipelines for AI/ML model development and deployment
Knowledge of data mesh principles or federated data architectures
Experience with cost optimization and cloud resource management for data workloads.
Demonstrated ability to inspire and influence at all levels of the organization, from engineers to executives
