The Data Engineer plays a lead role in building and operationalizing the data necessary for the enterprise data and analytics initiatives following industry standard practices and tools. The bulk of the data engineer’s work would be in building, managing and optimizing data pipelines and then moving these data pipelines effectively into production for key data and analytics consumers like business/data analysts, data scientists or any role that needs curated data for data and analytics use cases across the enterprise.
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The data engineer will be the key interface in operationalizing data and analytics on behalf of the business unit(s) and organizational outcomes. This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The data engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.
Additionally, data engineers will also be expected to collaborate with data scientists, data analysts and other data consumers and work on the models and algorithms developed by them in order to optimize them for data quality, security and governance and put them into production.
Advantages
The ideal candidate will be someone who has worked with a Data Scientest before.
Responsibilities
Key Responsibilities
Build data pipelines: These data pipelines must be created, maintained and optimized as workloads move from development to production for specific use cases. Architecting, creating and maintaining data pipelines will be the primary responsibility of the data engineer.
Drive Automation: The data engineer will be responsible for using innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management.
Educate and train: The data engineer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new data requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The data engineer will be required to train counterparts - such as data scientists, data analysts, LOB users or any data consumers - in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
Collaborate across departments: The data engineer will need strong collaboration skills in order to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with data science teams and with business (data) analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements.
Be a data and analytics evangelist: The data engineer will be considered a blend of data and analytics “evangelist,” “data guru” and “fixer.” This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.
Qualifications
Qualifications
Completion of a degree in data science, statistics, computer science, or a related quantitative discipline - or a combination of education, training and experience deemed equivalent
6-8 years in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks
3-5 years experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative
Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms
Demonstrated success in working with both IT and business while integrating analytics and data science output into business processes and workflows
Strong experience with various Data Management architectures like Data Warehouse, Data Lake, Data Hub and the supporting processes like Data Integration, Governance, Metadata Management
Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies including ETL/ELT, data replication/CDC, message-oriented data movement, API design and access
Strong experience with popular database programming languages including SQL, PL/SQL, others for relational databases and certifications on NoSQL/Hadoop oriented databases
Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Java, C++, Scala, and others
Strong experience in working with DevOps capabilities like version control, automated builds, testing and release management capabilities using tools like Git, Jenkins, Puppet, Ansible.
Basic experience working with popular data discovery, analytics and BI software tools like Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery.
ITIL Foundations, an asset
Current project management certification from a recognized institute (e.g. Project Management Professional (PMP) or PRINCE2), an asset
Summary
What we offer:
Competitive salary and bonus
Benefits and Pension Plan
Product Allowances & Safe Ride Home Program
An organization that cares about Corporate Social Responsibility
Tuition reimbursement
Training & Development Programs
An opportunity to learn about the world of wine
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.
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The Data Engineer plays a lead role in building and operationalizing the data necessary for the enterprise data and analytics initiatives following industry standard practices and tools. The bulk of the data engineer’s work would be in building, managing and optimizing data pipelines and then moving these data pipelines effectively into production for key data and analytics consumers like business/data analysts, data scientists or any role that needs curated data for data and analytics use cases across the enterprise.
The data engineer will be the key interface in operationalizing data and analytics on behalf of the business unit(s) and organizational outcomes. This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The data engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.
...
Additionally, data engineers will also be expected to collaborate with data scientists, data analysts and other data consumers and work on the models and algorithms developed by them in order to optimize them for data quality, security and governance and put them into production.
Advantages
The ideal candidate will be someone who has worked with a Data Scientest before.
Responsibilities
Key Responsibilities
Build data pipelines: These data pipelines must be created, maintained and optimized as workloads move from development to production for specific use cases. Architecting, creating and maintaining data pipelines will be the primary responsibility of the data engineer.
Drive Automation: The data engineer will be responsible for using innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management.
Educate and train: The data engineer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new data requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The data engineer will be required to train counterparts - such as data scientists, data analysts, LOB users or any data consumers - in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
Collaborate across departments: The data engineer will need strong collaboration skills in order to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with data science teams and with business (data) analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements.
Be a data and analytics evangelist: The data engineer will be considered a blend of data and analytics “evangelist,” “data guru” and “fixer.” This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.
Qualifications
Qualifications
Completion of a degree in data science, statistics, computer science, or a related quantitative discipline - or a combination of education, training and experience deemed equivalent
6-8 years in data management disciplines including data integration, modeling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks
3-5 years experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative
Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms
Demonstrated success in working with both IT and business while integrating analytics and data science output into business processes and workflows
Strong experience with various Data Management architectures like Data Warehouse, Data Lake, Data Hub and the supporting processes like Data Integration, Governance, Metadata Management
Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies including ETL/ELT, data replication/CDC, message-oriented data movement, API design and access
Strong experience with popular database programming languages including SQL, PL/SQL, others for relational databases and certifications on NoSQL/Hadoop oriented databases
Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Java, C++, Scala, and others
Strong experience in working with DevOps capabilities like version control, automated builds, testing and release management capabilities using tools like Git, Jenkins, Puppet, Ansible.
Basic experience working with popular data discovery, analytics and BI software tools like Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery.
ITIL Foundations, an asset
Current project management certification from a recognized institute (e.g. Project Management Professional (PMP) or PRINCE2), an asset
Summary
What we offer:
Competitive salary and bonus
Benefits and Pension Plan
Product Allowances & Safe Ride Home Program
An organization that cares about Corporate Social Responsibility
Tuition reimbursement
Training & Development Programs
An opportunity to learn about the world of wine
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.
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