Our client, is seeking an experienced, highly detail-oriented SAS Data Engineer to join their enterprise data platforms group to support a critical Enterprise Data Warehouse (EDW) modernization initiative.
...
In this high-impact technical role, you will be responsible for analyzing existing EDW data structures, assessing legacy SAS workloads, and converting complex SAS programs and macros into optimized PySpark pipelines running on a target Databricks data platform. Operating within a fast-paced banking environment, you will trace source-to-target data flows, map complex business logic, and conduct rigorous source-to-target data validation to ensure total functional equivalence and data reconciliation between legacy systems and the new cloud ecosystem.
Duration: 2-Month Contract (with potential for extension)
Work Arrangement: Hybrid (3 days per week on-site at the corporate office, 2 days remote)
Advantages
High-Impact Data Modernization: Direct the conversion of critical enterprise data pipelines from legacy SAS environments to modern Databricks and PySpark cloud architectures.
Top-Tier Financial Exposure: Gain valuable experience within a leading global banking institution's core data technology group.
Specialized Migration Scope: Leverage your niche expertise in both SAS legacy frameworks and modern PySpark code optimization.
Balanced Hybrid Model: Enjoy a predictable hybrid schedule combining remote work flexibility with 3 collaborative on-site days per week.
Responsibilities
EDW Technical Data Analysis: Analyze Enterprise Data Warehouse tables, underlying data structures, dependencies, data volumes, historical trends, and usage patterns.
SAS Workload Assessment: Conduct thorough code reviews of existing SAS programs, macros, datasets, and execution dependencies to extract core business and technical transformation logic.
SAS-to-PySpark Conversion: Translate and refactor legacy SAS transformation and processing logic into clean, scalable PySpark code on the Databricks platform while ensuring complete functional equivalence.
Data Mapping & Lineage: Trace and document source-to-target data flows, complex joins, filter conditions, and downstream dependencies across EDW and SAS workloads.
Data Validation & Reconciliation: Execute rigorous source-to-target data profiling and reconciliation to validate PySpark outputs against existing SAS and EDW baseline results.
Qualifications
Professional Experience: 6 to 9 years of dedicated data engineering experience, preferably within the banking or financial services industry.
Legacy SAS Mastery: Deep technical proficiency in reading, analyzing, and deciphering complex SAS programs, macros, datasets, and data processing pipelines.
PySpark & Databricks Expertise: Strong hands-on experience authoring, optimizing, and deploying PySpark code within a Databricks environment.
EDW & Data Architecture: Solid understanding of Enterprise Data Warehouse concepts, relational data structures, source-to-target mapping, and data lineage tracing.
Reconciliation & Quality Control: Demonstrated track record performing data validation, automated quality checks, and output reconciliation between legacy and target systems.
Summary
If you are a tech-savvy SAS Data Engineer who pairs 6 to 9 years of financial services experience with expert SAS legacy code analysis and hands-on PySpark/Databricks development skills, this 2-month hybrid contract opportunity is an ideal next step. Bring your pipeline conversion precision, data validation rigor, and collaborative mindset to our client's data modernization team today!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
Our client, is seeking an experienced, highly detail-oriented SAS Data Engineer to join their enterprise data platforms group to support a critical Enterprise Data Warehouse (EDW) modernization initiative.
In this high-impact technical role, you will be responsible for analyzing existing EDW data structures, assessing legacy SAS workloads, and converting complex SAS programs and macros into optimized PySpark pipelines running on a target Databricks data platform. Operating within a fast-paced banking environment, you will trace source-to-target data flows, map complex business logic, and conduct rigorous source-to-target data validation to ensure total functional equivalence and data reconciliation between legacy systems and the new cloud ecosystem.
Duration: 2-Month Contract (with potential for extension)
Work Arrangement: Hybrid (3 days per week on-site at the corporate office, 2 days remote)
Advantages
High-Impact Data Modernization: Direct the conversion of critical enterprise data pipelines from legacy SAS environments to modern Databricks and PySpark cloud architectures.
...
Top-Tier Financial Exposure: Gain valuable experience within a leading global banking institution's core data technology group.
Specialized Migration Scope: Leverage your niche expertise in both SAS legacy frameworks and modern PySpark code optimization.
Balanced Hybrid Model: Enjoy a predictable hybrid schedule combining remote work flexibility with 3 collaborative on-site days per week.
Responsibilities
EDW Technical Data Analysis: Analyze Enterprise Data Warehouse tables, underlying data structures, dependencies, data volumes, historical trends, and usage patterns.
SAS Workload Assessment: Conduct thorough code reviews of existing SAS programs, macros, datasets, and execution dependencies to extract core business and technical transformation logic.
SAS-to-PySpark Conversion: Translate and refactor legacy SAS transformation and processing logic into clean, scalable PySpark code on the Databricks platform while ensuring complete functional equivalence.
Data Mapping & Lineage: Trace and document source-to-target data flows, complex joins, filter conditions, and downstream dependencies across EDW and SAS workloads.
Data Validation & Reconciliation: Execute rigorous source-to-target data profiling and reconciliation to validate PySpark outputs against existing SAS and EDW baseline results.
Qualifications
Professional Experience: 6 to 9 years of dedicated data engineering experience, preferably within the banking or financial services industry.
Legacy SAS Mastery: Deep technical proficiency in reading, analyzing, and deciphering complex SAS programs, macros, datasets, and data processing pipelines.
PySpark & Databricks Expertise: Strong hands-on experience authoring, optimizing, and deploying PySpark code within a Databricks environment.
EDW & Data Architecture: Solid understanding of Enterprise Data Warehouse concepts, relational data structures, source-to-target mapping, and data lineage tracing.
Reconciliation & Quality Control: Demonstrated track record performing data validation, automated quality checks, and output reconciliation between legacy and target systems.
Summary
If you are a tech-savvy SAS Data Engineer who pairs 6 to 9 years of financial services experience with expert SAS legacy code analysis and hands-on PySpark/Databricks development skills, this 2-month hybrid contract opportunity is an ideal next step. Bring your pipeline conversion precision, data validation rigor, and collaborative mindset to our client's data modernization team today!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
