Design, develop, and optimize scalable Azure Databricks data pipelines, lakehouse solutions, and cloud data platforms. Build batch and streaming ETL/ELT processes using Spark, PySpark, Delta Lake, Auto Loader, and Structured Streaming. Implement data modeling, SCD, CDC, governance, Unity Catalog, integrations, CI/CD, monitoring, testing, and performance tuning while collaborating with architects and stakeholders.
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.
Kumaran Systems Toronto, Ontario, CAN Office
703 Evans Avenue, Suite 605 , Toronto, ON , Canada, M9C 5E9
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