Rq08100: Software Developer - Etl

About the job RQ08100: Software Developer - ETLGeneral ResponsibilitiesThis role is responsible for designing, developing, maintaining, and optimizing ETL (Extract, Transform, Load) processes in Databricks for data warehousing, data lakes, and analytics. The developer will work closely with data architects and business teams to ensure the efficient transformation and movement of data to meet business needs, including handling Change Data Capture (CDC) and streaming data.
Azure Databricks, Delta Lake, Delta Live Tables, and Spark to process structured and unstructured data.
Azure Databricks/PySpark (good Python/PySpark knowledge required) to build transformations of raw data into curated zone in the data lake.
Azure Databricks/PySpark/SQL (good SQL knowledge required) to develop and/or troubleshoot transformations of curated data into FHIR.
Understand the requirements. Recommend changes to models to support ETL design.
Define primary keys, indexing strategies, and relationships that enhance data integrity and performance across layers.
Define the initial schemas for each data layer.
Assist with data modelling and updates of source-to-target mapping documentation.
Document and implement schema validation rules to ensure incoming data conforms to expected formats and standards.
Design data quality checks within the pipeline to catch inconsistencies, missing values, or errors early in the process.
Proactively communicate with business and IT experts on any changes required to conceptual, logical and physical models, communicate and review timelines, dependencies, and risks.
Development of ETL strategy and solution for different sets of data modules
Understand the Tables and Relationships in the data model.
Create low level design documents and test cases for ETL development.
Implement error-catching, logging, retry mechanisms, and handling data anomalies.
Create the workflows and pipeline design.
Development and testing of data pipelines with Incremental and Full Load.
Develop high quality ETL mappings/scripts/notebooks.
Develop and maintain pipeline from Oracle data source to Azure Delta Lakes and FHIR.
Perform unit testing.
Ensure performance monitoring and improvement.
Performance review, data consistency checks
Troubleshoot performance issues, ETL issues, log activity for each pipeline and transformation.
Review and optimize overall ETL performance.
End-to-end integrated testing for Full Load and Incremental LoadNo specific tasks listed.
Plan for Go Live, Production Deployment.
Configure parameters, scripts for go live. Test and review the instructions.
Create release documents and help build and deploy code across servers.
Go Live Support and Review after Go Live.
Review existing ETL process, tools and provide recommendation on improving performance and reduce ETL timelines.
Review infrastructure and remediate issues for overall process improvement.
Knowledge Transfer to Ministry staff, development of documentation on the work completed.
Document work and share the ETL end-to-end design, troubleshooting steps, configuration and scripts review.
Transfer documents, scripts and review of documents to Ministry.
SkillsExperience and Skill Set RequirementsPlease note this role is part of a Hybrid Work Arrangement and resource(s) will be required to work at a minimum of 3 days per week at the Office Location.
Must Have Skills
7+ years using ETL tools such as Microsoft SSIS, stored procedures, T-SQL.
2+ Delta Lake, Databricks and Azure Databricks pipelines.
2+ years Python and PySpark.
Solid understanding of the Medallion Architecture (Bronze, Silver, Gold) and experience implementing it in production environments.
Hands‑on experience with CDC tools (e.G., GoldenGate) for managing real‑time data.
SQL Server, Oracle.
Experience:
Experience of 7+ years of working with SQL Server, T‑SQL, Oracle, PL/SQL development or similar relational databases.
Experience of 2+ years of working with Azure Data Factory, Databricks and Python development.
Experience building data ingestion and change data capture using Oracle Golden Gate.
Experience in designing, developing, and implementing ETL pipelines using Databricks and related tools to ingest, transform, and store large‑scale datasets.
Experience in leveraging Databricks, Delta Lake, Delta Live Tables, and Spark to process structured and unstructured data.
Experience working with building databases, data warehouses and working with delta and full loads.
Experience on Data modeling, and tools e.G. SAP Power Designer, Visio, or similar.
Experience working with SQL Server SSIS or other ETL tools, solid knowledge and experience with SQL scripting.
Experience developing in an Agile environment.
Understanding data warehouse architecture with a delta lake.
Ability to analyze, design, develop, test and document ETL pipelines from detailed and high‑level specifications, and assist in troubleshooting.
Ability to utilize SQL to perform DDL tasks and complex queries.
Good knowledge of database performance optimization techniques.
Ability to assist in the requirements analysis and subsequent developments.
Ability to conduct unit testing and assist in test preparations to ensure data integrity.
Work closely with Designers, Business Analysts and other Developers.
Liaise with Project Managers, Quality Assurance Analysts and Business Intelligence Consultants.
Design and implement technical enhancements of Data Warehouse as required.
Development, Database and ETL experience (60 points)
Experience in developing and managing ETL pipelines, jobs, and workflows in Databricks.
Deep understanding of Delta Lake for building data lakes and managing ACID transactions, schema evolution, and data versioning.
Experience automating ETL pipelines using Delta Live Tables, including handling Change Data Capture (CDC) for incremental data loads.
Proficient in structuring data pipelines with the Medallion Architecture to scale data pipelines and ensure data quality.
Hands‑on experience developing streaming tables in Databricks using Structured Streaming and readStream to handle real‑time data.
Expertise in integrating CDC tools like GoldenGate or Debezium for processing incremental updates and managing real‑time data ingestion.
Experience using Unity Catalog to manage data governance, access control, and ensure compliance.
Skilled in managing clusters, jobs, autoscaling, monitoring, and performance optimization in Databricks environments.
Knowledge of using Databricks Autoloader for efficient batch and real‑time data ingestion.
Experience with data governance best practices, including implementing security policies, access control, and auditing with Unity Catalog.
Proficient in creating and managing Databricks Workflows to orchestrate job dependencies and schedule tasks.
Strong knowledge of Python, PySpark, and SQL for data manipulation and transformation.
Experience integrating Databricks with cloud storage solutions such as Azure Blob Storage, AWS S3, or Google Cloud Storage.
Familiarity with external orchestration tools like Azure Data Factory.
Implementing logical and physical data models.
Knowledge of FHIR is an asset.
Design Documentation and Analysis Skills (20 points)
Demonstrated experience in creating design documentation such as:
Schema definitions
Error handling and logging
ETL Process Documentation
Job Scheduling and Dependency Management
Data Quality and Validation Checks
Performance Optimization and Scalability Plans
Troubleshooting Guides
Data Lineage
Security and Access Control Policies applied within ETL
Experience in Fit‑Gap analysis, system use case reviews, requirements reviews, coding exercises and reviews.
Participate in defect fixing, testing support and development activities for ETL.
Analyze and document solution complexity and interdependencies including providing support for data validation.
Strong analytical skills for troubleshooting, problem‑solving, and ensuring data quality.
Certifications (10 points)
Databricks Certified Professional Data Engineer
AWS Certified Data Analytics - Specialty
Google Cloud Professional Data Engineer
Communication, Leadership Skills and Knowledge Transfer (10 points)
Ability to collaborate effectively with cross‑functional teams and communicate complex technical concepts to non‑technical stakeholders.
Strong problem‑solving skills and experience working in an Agile or Scrum environment.
Ability to provide technical guidance and support to other team members on Databricks best practices.
Must have previous work experience in conducting Knowledge Transfer sessions, ensuring the resources will receive the required knowledge to support the system.
Must develop documentation and materials as part of a review and knowledge transfer to other members.
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