Data Manager - Databricks
Unison Group Open On-site Bengaluru Manager Data Engineering
At a glance
Manager-level Data Engineering role at Unison Group. Bengaluru.
Pay not stated Data Engineering salary
RoleData Engineering
SeniorityManager
LocationBengaluru
WorkplaceOn-site
PostedOct 16, 2026
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Lead data engineers, set their technical direction and reinforce sound engineering habits
- Build and run Databricks Lakehouse pipelines with Spark, Delta Lake, SQL, Python and cloud services
- Set practices for data ingestion, transformation, testing, release, monitoring and production support
- Turn stakeholder needs into data solutions while embedding quality, security, governance and privacy controls
- Review designs and code, improve workload performance and cost, and guide Agile planning and technical roadmaps
What you bring
- You hold a bachelor's or master's degree in computer science, engineering, IT, data science or a related discipline
- You bring substantial data engineering experience, including leading or managing an engineering team
- You have strong practical experience with Databricks and Lakehouse architectures
- You are advanced in Spark or PySpark, SQL, Python, Delta Lake, ETL/ELT, pipeline development and orchestration
- You have optimized large-scale data workloads for performance, reliability and cost
Who this fits
This manager role suits someone who combines hands-on data engineering with team leadership, architecture reviews and delivery ownership. The position is based onsite in Bengaluru.
From the employer
Role Summary
We are seeking an experienced Data Engineering Manager with strong Databricks and modern cloud data engineering expertise to lead the design, development and operation of scalable enterprise data solutions. The role combines technical leadership, people management, architecture oversight and delivery accountability. The successful candidate will establish engineering standards, reusable frameworks and robust practices for secure, governed, reliable and cost-efficient Lakehouse solutions.
Key Responsibilities
- Lead, mentor and support a team of data engineers, setting technical direction and encouraging engineering best practices.
- Design, build and operate scalable data pipelines and Lakehouse solutions using Databricks, Apache Spark, Delta Lake, SQL, Python and cloud-native services.
- Define standards for ingestion, transformation, orchestration, automated testing, deployment, observability, monitoring and production support.
- Collaborate with business, analytics, data science, architecture, security and platform teams to translate requirements into high-quality data solutions.
- Champion reusable and metadata-driven frameworks, automation, Medallion Architecture patterns and modern data engineering practices.
- Embed data quality, security, governance, privacy and compliance controls across pipelines and data products.
- Review solution designs, code and deployment approaches to maintain consistency, resilience, maintainability and operational reliability.
- Optimize Databricks workloads for performance, scalability, reliability and cost efficiency.
- Support Agile delivery, including backlog refinement, sprint planning, estimation, risk management, continuous improvement and technical roadmap development.
Required Qualifications & Experience
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Science or a related discipline.
- Significant professional experience in data engineering, including experience leading or managing data engineering teams.
- Strong hands-on experience with Databricks and Lakehouse-based architectures.
- Advanced proficiency in Apache Spark / PySpark, SQL, Python, Delta Lake, ETL/ELT, data pipeline development and orchestration.
- Strong understanding of Medallion Architecture, distributed processing and modern cloud data-platform patterns.
- Experience implementing CI/CD, automated testing, logging, monitoring, alerting and controlled deployment practices.
- Working knowledge of data governance, lineage, data quality, security, privacy and role-based access control.
- Proven experience optimizing large-scale workloads for performance, reliability and cost.
- Experience conducting design and code reviews and establishing engineering standards.
- Strong stakeholder management, problem-solving and written and verbal communication skills.
- Preferred Qualifications
- Experience with Azure Databricks, Unity Catalog and the broader Azure data ecosystem.
- Experience building reusable, configuration-driven or metadata-driven ingestion and transformation frameworks.
- Exposure to Azure DevOps, Git-based CI/CD, infrastructure as code and automated release controls.
- Experience supporting production-grade data platforms and operational support models.
- Understanding of dimensional modelling, enterprise data modelling and semantic or data-serving layers.
- Databricks or relevant cloud certification.
- Experience delivering governed data products for Power BI, analytics, AI/ML or advanced analytics.
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