Data Engineering Manager - Databricks & Lakehouse

Unison Group; Bengaluru; Hybride; INR 2 500 000 - 4 200 000; Plein temps

Résumé du poste

Unison Group in Bengaluru seeks an experienced Data Engineering Manager to lead design, development and operation of scalable enterprise data solutions. You will establish standards, reusable frameworks and robust governance across Lakehouse platforms using Databricks and cloud-native services.

You will mentor a team of data engineers, shape the technical direction and ensure delivery against commitments while optimizing workloads for performance and cost.

Qualifications

• 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 leadership/management of data engineering teams.
• Strong hands-on experience with Databricks and Lakehouse architectures.
• Advanced proficiency in Spark/PySpark, SQL, Python, Delta Lake, ETL/ELT, data pipelines and orchestration.
• Understanding of Medallion Architecture, distributed processing and cloud data-platform patterns.
• Experience implementing CI/CD, automated testing, logging, monitoring and controlled deployment practices.
• Knowledge of data governance, lineage, data quality, security and role-based access control.
• Proven experience optimizing large-scale workloads for performance and cost.
• Experience conducting design and code reviews and establishing engineering standards.
• Strong stakeholder management and communication skills.

Responsabilités

• Lead, mentor and support a team of data engineers, setting technical direction.
• Design, build and operate scalable data pipelines and Lakehouse solutions using Databricks and cloud-native services.
• Define standards for ingestion, transformation, orchestration, testing, deployment and monitoring.
• Collaborate with business, analytics, data science, architecture, security and platform teams.
• Champion reusable and metadata-driven frameworks and modern data engineering practices.
• Embed data quality, security, governance and compliance controls across pipelines.
• Review designs, code and deployment approaches to ensure reliability and maintainability.
• Optimize Databricks workloads for performance, scalability and cost efficiency.
• Support Agile delivery including backlog refinement and sprint planning.

Connaissances

Databricks

Apache Spark

Delta Lake

SQL

Python

CI/CD

Data governance

Data quality

Orchestration

Medallion Architecture

Formation

Bachelor's/Master's in CS/Engineering/IT/Data Science

Outils

Azure Databricks

Unity Catalog

Azure DevOps

Git-based CI/CD

Description du poste

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.