Azure Developer

Job Information

Department Name

Banking

Date Opened

06/02/2026

Industry

IT Services

Job Type

Full time

Work Experience

5+ years

Required Skills

Apache Spark

Spark

+9

City

Toronto

State/Province

Ontario

Country

Canada

Zip/Postal Code

M4C

About Us

Kumaran Systems: Engineering Future-Ready Transformations Since 1992, Kumaran Systems has been helping enterprises bridge the past and the future, reimagining legacy systems and embracing AI-led possibilities. With over 34 years of delivery excellence, we specialise in re-engineering core applications, driving cloud transformation, and building automation-first, GenAI-enabled ecosystems across the Banking and Financial Services, Insurance, Telecom, and Automotive sectors. We help organisations not just upgrade their systems but reimagine what’s possible.

Why Kumaran?

We blend engineering discipline with AI innovation, helping clients modernise with confidence, automate with clarity, and scale with purpose. Our global delivery model ensures agility, responsiveness, and seamless collaboration, with clients always at the heart of every engagement. At Kumaran, we don’t just solve problems, we engineer future-ready transformations.

Job Description

We are seeking an experienced Databricks developer with strong expertise in Azure Databricks, Apache Spark, and modern data engineering practices. The ideal candidate will be responsible for designing, developing, and optimising scalable data pipelines, data lakehouse solutions, and cloud-based data platforms. The role requires hands-on experience with Spark processing, Delta Lake, Unity Catalogue, data modelling, and enterprise-grade data integration solutions.

Key Responsibilities

Data Engineering & Databricks Development

Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, Apache Spark, and SQL.

Build and optimize batch and streaming data pipelines using PySpark, Spark Structured Streaming, and Auto Loader.

Develop and support enterprise data lakehouse solutions using Delta Lake and Databricks technologies.

Implement data ingestion, transformation, cleansing, and aggregation processes for large-scale datasets.

Develop reusable frameworks and best practices for data engineering solutions.

Data Modeling & Performance Optimization

Design and implement data models to support reporting, analytics, and business requirements.

Build and maintain Slowly Changing Dimensions (SCD Type 1 & Type 2) for data warehousing solutions.

Develop and optimize Change Data Capture (CDC) pipelines.

Optimize Spark workloads through partitioning, clustering, caching, and performance tuning techniques.

Ensure efficient query performance and scalability across large datasets.

Unity Catalog & Data Governance

Configure and manage Databricks Unity Catalog environments.

Create and manage catalogs, schemas, tables, materialized views, functions, and volumes.

Implement enterprise data governance, security, access control, and compliance standards.

Support metadata management and data lineage initiatives across the data platform.

Cloud & Integration

Develop cloud-native data solutions on Microsoft Azure and related cloud services.

Integrate data from multiple internal and external data sources.

Implement Lakehouse Federation and foreign catalogs to access external data platforms.

Collaborate with architects and stakeholders to design scalable cloud data solutions.

DevOps & Operational Excellence

Support CI/CD implementation and automated deployment processes.

Participate in code reviews, testing, and release activities.

Monitor and troubleshoot data pipeline failures and performance issues.

Ensure adherence to development standards, security policies, and operational best practices.

Required Qualifications

Strong hands-on experience with Databricks and Apache Spark (PySpark and/or Scala).

Extensive experience with SQL and complex data transformation techniques.

Experience in ETL/ELT development and enterprise data pipeline implementation.

Strong experience with Microsoft Azure and cloud-based data platforms.

Hands-on experience with Azure Databricks and Delta Lake.

Experience building batch processing pipelines using Auto Loader and real-time pipelines using Spark Structured Streaming.

Strong understanding of data warehousing concepts and dimensional modeling.

Experience implementing SCD Type 1, SCD Type 2, and CDC processes.

Strong knowledge of Spark performance tuning, partitioning, and optimization techniques.

Experience with CI/CD pipelines and DevOps practices.

Strong analytical, troubleshooting, and problem-solving skills.