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2024-48 - Data Analyst
Remote, AlbertaJob Description: Designing Scalable Data Architecture: Develop and implement cloud-based data architectures on GCP to support banking analytics and reporting needs.
Data Modeling & Structuring: Build data models optimized for performance, scalability, and compliance with banking regulations
Cloud Data Platform Design: Architect solutions using GCP services such as BigQuery to ingest, process, and store large-scale data for financial analytics.
ETL/ELT Pipelines: Design, develop, and manage robust ETL/ELT pipelines to extract data from various banking sources, transform it into required formats, and load it into data lakes (GCP).
Data Integration: Integrate data from disparate banking systems (SAP Banking Systems, CRM, Salesforce) and external sources into GCP.
Data Quality Assurance: Implement data validation checks, monitoring, and alerts to ensure the accuracy, consistency, and reliability of financial data.
Data Visualization: Design and develop Tableau dashboards to provide actionable insights into banking metrics, including loan performance, credit risk, profitability, and customer segmentation.
Interactive Reports for Stakeholders: Build interactive, easy-to-understand reports for both technical and non-technical banking stakeholders (finance, risk management, operations, senior executives, etc.).
Liaise with Business Stakeholders: Regularly engage with business stakeholders (finance, risk management, operations etc.) to ensure the data solutions meet the specific needs of the banking business.
Production Support: Provide ongoing support for deployed data pipelines and Tableau dashboards, troubleshooting issues related to data quality, performance, and integration.
Incident Management: Handle production incidents promptly, resolving data issues and ensuring the availability of key banking metrics for reporting and decision-making.
Stay Current with Industry Trends: Keep up to date with the latest developments in data engineering, cloud technologies (especially GCP), and data visualization trends, ensuring that the team is leveraging best practices.
Innovation: Continuously identify opportunities to innovate the data architecture and data analytics workflows, exploring new tools, technologies, and methodologies that can improve the efficiency and scalability of the banking data infrastructure.
Technical Documentation: Create comprehensive documentation for data pipelines, architecture designs, and Tableau reports, ensuring knowledge sharing within the team and easy onboarding for new team members.
Best Practices: Document and promote best practices in data engineering, security, cloud optimization, and dashboard development to ensure a high standard of work.
Top Skills: Five or more years of experience in data engineering or software development, preferably in the banking, finance, or other regulated industries. Proven experience working with large-scale data systems and cloud platforms (preferably Google Cloud Platform). Experience in financial services is highly desirable (working with transactional data, credit risk, regulatory compliance, etc.). Experience in business performance reports, process improvement and risk assessment is highly desirable. Advanced knowledge of ETL (Extract, Transform, Load) and ELT processes, including data ingestion, transformation, and cleaning for large-scale datasets. Experience in managing data pipelines, ensuring data quality and reliability across multiple systems. Tableau: Proficiency in developing interactive dashboards and reports to visualize key business and financial metrics. Strong understanding of data governance best practices, ensuring data quality, consistency, and compliance with industry standards. Experience in implementing data validation checks, automated testing, and monitoring frameworks to ensure the integrity of data across pipelines. Ability to transform business requirements into technical solutions, collaborating with cross-functional teams (finance, risk management, operations, etc.). Experience with financial analytics: Knowledge of banking-specific metrics such as credit risk, impaired loans, and operational KPIs.
Data Modeling & Structuring: Build data models optimized for performance, scalability, and compliance with banking regulations
Cloud Data Platform Design: Architect solutions using GCP services such as BigQuery to ingest, process, and store large-scale data for financial analytics.
ETL/ELT Pipelines: Design, develop, and manage robust ETL/ELT pipelines to extract data from various banking sources, transform it into required formats, and load it into data lakes (GCP).
Data Integration: Integrate data from disparate banking systems (SAP Banking Systems, CRM, Salesforce) and external sources into GCP.
Data Quality Assurance: Implement data validation checks, monitoring, and alerts to ensure the accuracy, consistency, and reliability of financial data.
Data Visualization: Design and develop Tableau dashboards to provide actionable insights into banking metrics, including loan performance, credit risk, profitability, and customer segmentation.
Interactive Reports for Stakeholders: Build interactive, easy-to-understand reports for both technical and non-technical banking stakeholders (finance, risk management, operations, senior executives, etc.).
Liaise with Business Stakeholders: Regularly engage with business stakeholders (finance, risk management, operations etc.) to ensure the data solutions meet the specific needs of the banking business.
Production Support: Provide ongoing support for deployed data pipelines and Tableau dashboards, troubleshooting issues related to data quality, performance, and integration.
Incident Management: Handle production incidents promptly, resolving data issues and ensuring the availability of key banking metrics for reporting and decision-making.
Stay Current with Industry Trends: Keep up to date with the latest developments in data engineering, cloud technologies (especially GCP), and data visualization trends, ensuring that the team is leveraging best practices.
Innovation: Continuously identify opportunities to innovate the data architecture and data analytics workflows, exploring new tools, technologies, and methodologies that can improve the efficiency and scalability of the banking data infrastructure.
Technical Documentation: Create comprehensive documentation for data pipelines, architecture designs, and Tableau reports, ensuring knowledge sharing within the team and easy onboarding for new team members.
Best Practices: Document and promote best practices in data engineering, security, cloud optimization, and dashboard development to ensure a high standard of work.
Top Skills: Five or more years of experience in data engineering or software development, preferably in the banking, finance, or other regulated industries. Proven experience working with large-scale data systems and cloud platforms (preferably Google Cloud Platform). Experience in financial services is highly desirable (working with transactional data, credit risk, regulatory compliance, etc.). Experience in business performance reports, process improvement and risk assessment is highly desirable. Advanced knowledge of ETL (Extract, Transform, Load) and ELT processes, including data ingestion, transformation, and cleaning for large-scale datasets. Experience in managing data pipelines, ensuring data quality and reliability across multiple systems. Tableau: Proficiency in developing interactive dashboards and reports to visualize key business and financial metrics. Strong understanding of data governance best practices, ensuring data quality, consistency, and compliance with industry standards. Experience in implementing data validation checks, automated testing, and monitoring frameworks to ensure the integrity of data across pipelines. Ability to transform business requirements into technical solutions, collaborating with cross-functional teams (finance, risk management, operations, etc.). Experience with financial analytics: Knowledge of banking-specific metrics such as credit risk, impaired loans, and operational KPIs.