Role overview

Required Experience:

Senior-level Oracle experience, including complex SQL, PL/SQL, data extraction, performance tuning, data profiling, and analysis of production data structures.
Hands-on experience developing AWS Glue Python Shell jobs and Python-based ETL processes that extract, transform, validate, and load data into Amazon Redshift.
Strong experience with Amazon Redshift, AWS DMS, dimensional modelling, star schemas, incremental data loads, reconciliation, and data-quality controls.
Strong Power BI experience, including semantic models, DAX measures, Power Query, report design, dashboard development, refresh processes, and performance optimization.
Practical experience interpreting the Ontario HRM Standard, FHIR data structures, and HL7 v2 data structures for healthcare reporting and analytics.

Responsibilities:

Work with OCRE business and technical stakeholders to define reporting requirements, business measures, data-quality indicators, acceptance criteria, and dashboard priorities.
Analyze Oracle schemas and OCRE application data sources to identify authoritative fields, relationships, business rules, data limitations, and extraction requirements.
Develop, test, and support AWS Glue Python Shell jobs that extract data from Oracle and OCRE application sources and load it into Amazon Redshift.
Configure or support AWS DMS replication and change-data-capture processes where they are used to move Oracle or application data into the reporting platform.
Implement full and incremental load patterns, transformations, validation rules, error handling, logging, reconciliation, restartability, and operational monitoring for data pipelines.
Design and maintain the Amazon Redshift reporting model, including staging structures, fact and dimension structures, keys, history handling, and star-schema relationships.
Create and optimize Redshift SQL views, materialized views, stored procedures, and queries that expose governed reporting datasets to Power BI.
Develop Power BI semantic models, DAX measures, Power Query transformations, reports, and dashboards for OCRE metrics, operational performance, and data quality.
Translate Ontario HRM, FHIR, and HL7 v2 data elements into consistent analytical definitions while preserving clinical meaning, traceability, and source-system context.
Profile source and target data, identify anomalies or gaps, implement data-quality rules, and reconcile dashboard results to Oracle and OCRE source data.
Tune Oracle queries, AWS Glue jobs, Redshift workloads, and Power BI models to meet agreed performance, refresh, scalability, and support requirements.
Prepare technical designs, source-to-target mappings, dimensional models, metric definitions, test evidence, deployment instructions, runbooks, and knowledge-transfer materials.

Desired Skills:

Experience designing dimensional models using fact tables, dimension tables, conformed dimensions, surrogate keys, slowly changing dimensions, and auditable metric definitions.
Experience with Redshift performance features such as distribution styles, sort keys, workload management, query plans, materialized views, and columnar data design.
Experience with Power BI row-level security, deployment pipelines, scheduled refresh, gateways, workspace governance, usage monitoring, and accessibility practices.
Experience supporting healthcare analytics solutions that require data lineage, privacy-aware access, auditability, reconciliation, production monitoring, and incident investigation.

Required Skills:

Oracle, Python, AWS Glue Python Shell, and AWS DMS experience, including complex SQL, source-data analysis, extraction, transformation, incremental loading, validation, reconciliation, and production support.
Amazon Redshift and dimensional-modelling experience, including staging layers, fact and dimension structures, star schemas, views or materialized views, SQL optimization, and reporting-layer performance tuning.
Power BI development experience, including semantic modelling, DAX, Power Query, dashboards, operational metrics, data-quality reporting, refresh processes, security, and performance optimization.
Healthcare-data experience involving the Ontario HRM Standard, FHIR data structures, and HL7 v2 data structures, including mapping source data to consistent analytical definitions and metrics.

Rated Criteria:

Oracle, Python, AWS Glue Python Shell, and AWS DMS experience, including source analysis, complex SQL, ETL development, incremental loading, validation, reconciliation, and production support. 25 points
Amazon Redshift and dimensional-modelling experience, including staging structures, fact and dimension models, star schemas, views or materialized views, SQL optimization, and workload performance. 25 points
Power BI experience, including semantic models, DAX, Power Query, dashboards, operational metrics, data-quality reporting, refresh, security, and performance optimization. 25 points
Ontario HRM Standard, FHIR, and HL7 v2 data-structure experience, including healthcare-data mapping, metric definition, traceability, and interpretation of clinical and operational data. 25 points

Total: 100 Points

RequirementsDeliverables

Production-ready AWS Glue Python Shell jobs and supporting Python or SQL components that extract data from Oracle and OCRE application sources and load it into Amazon Redshift.
AWS DMS configurations, mappings, validation results, and operational documentation for replication or change-data-capture workflows assigned to the resource.
Amazon Redshift staging, ingestion, transformation, and reconciliation components supporting full and incremental data loads.
A governed Redshift star-schema reporting layer containing the required fact and dimension structures, relationships, history handling, and business definitions.
Optimized Redshift views, materialized views, stored procedures, and queries that provide Power BI with reliable and supportable reporting datasets.
Power BI semantic models, DAX measures, Power Query transformations, reports, and dashboards for OCRE metrics, operational monitoring, data quality, and stakeholder-defined reporting needs.
Documented metric definitions, source-to-target mappings, data-quality rules, validation controls, lineage, and reconciliation results.
Automated and manual test assets covering extraction, transformation, incremental loads, data completeness, data accuracy, report calculations, refresh, regression, and release validation.
Operational logging, monitoring, alerting, restart, recovery, incident-investigation, and support procedures for the data pipelines and reporting solution.
Technical designs, dimensional models, configuration notes, deployment instructions, runbooks, demonstrations, defect fixes, release support, and knowledge-transfer materials.

Must Haves:

Oracle, Python, AWS Glue Python Shell, and AWS DMS experience, including complex SQL, source-data analysis, extraction, transformation, incremental loading, validation, reconciliation, and production support.
Amazon Redshift and dimensional-modelling experience, including staging layers, fact and dimension structures, star schemas, views or materialized views, SQL optimization, and reporting-layer performance tuning.
Power BI development experience, including semantic modelling, DAX, Power Query, dashboards, operational metrics, data-quality reporting, refresh processes, security, and performance optimization.
Healthcare-data experience involving the Ontario HRM Standard, FHIR data structures, and HL7 v2 data structures, including mapping source data to consistent analytical definitions and metrics.