Title: Data Engineer
Location: Remote, Canada
Type of contract: Permanent full time
Salary band: $71,298 – $89,122/year
The Canadian Red Cross (CRC), an iconic brand and one of the most inspirational not-for-profit organizations on this planet, is seeking a Data Engineer to join our team.
You have a passion for helping others, and want to work with other dedicated individuals? By applying, you are taking a big first step to be part of an exciting organization.
At the Canadian Red Cross, we are guided by our fundamental principles of humanity, impartiality, neutrality, independence, voluntary service, unity, and universality. We help people and communities in Canada and around the world in times of need and support them in strengthening their resilience.
Starting with the hiring process, we are committed to having an accessible, diverse, inclusive, and barrier-free work environment where everyone can reach their full potential. We encourage all qualified persons to apply, particularly Indigenous Peoples, persons with disabilities, racialized people, and people with diverse gender identities and sexual orientations, and others who share our values and contribute to fostering an inclusive and diverse workplace.
Reporting directly to the Data and Analytics Lead, the Data Engineer will play a pivotal role in building and operationalizing the data necessary for the CRC’s data and analytics initiatives following industry standard practices and tools. They will be responsible for building, managing, and optimizing data pipelines before moving these data pipelines effectively into production for key data and analytics consumers like business/data analysts, data scientists or any stakeholder needing curated data for data and analytics use cases across the organization.
The incumbent will ensure compliance with data governance and data security requirements while creating, improving, and operationalizing these integrated and reusable data pipelines in order to enable faster data access, integrated data reuse and vastly improved time-to-solution for data and analytics initiatives. They will be evaluated on their ability to integrate analytics and (or) data science results within business processes.
The Data Engineer will act as the key interface in operationalizing data and analytics on behalf of the business unit(s) and organizational outcomes. This role requires to work creatively and collaboratively with business stakeholders, IT experts, and subject-matter experts across the organization to plan and deliver optimal analytics and data science solutions. It will involve standardizing effective data management practices and promoting better understanding of data and analytics.
Additionally, the Data Engineer is expected to collaborate with data scientists, data analysts and other data consumers and to work on the models and algorithms developed by them to optimize them for data quality, security and governance and put them into production leading to potentially large productivity gains.
As a Data Engineer you will:
- Build data pipelines: Managed data pipelines consist of a series of stages through which data flows (for example, from data sources or endpoints of acquisition to integration to consumption for specific use cases).
- Drive Automation through effective metadata management: The Data Engineer will use 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.
- They will also help renovate the data management infrastructure to drive automation in data integration and management. This will include (but not limited to):
- Using modern data preparation, integration and AI-enabled metadata management tools and techniques.
- Tracking data consumption patterns.
- Performing intelligent sampling and caching.
- Monitoring schema changes.
- Recommending — or sometimes even automating — existing and future integration flows.
- 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 to address new data requirements. They will be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration, and operationalization techniques in order to optimally address these data requirements. The Data Engineer will be required to train counterparts such as data scientists, data analysts, business [ML1] users or any data consumers in these data pipelining and preparation techniques, to make it easier for them to integrate and consume the data they need for their own use cases.
- Participate in ensuring compliance and governance during data use : The Data Engineer will be responsible to ensure that users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives.
- Become a data and analytics expert: The Data Engineer will possess a blend of data and analytics expertise.
What we are looking for:
- A bachelor’s or master’s degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field; a Master’s is preferred.
- 6+ years of work experience 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+ years of experience working in cross-functional teams and collaborating with business and IT stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative.
- The ideal candidate will have a combination of IT skills, data governance skills, and analytics skills with a technical or computer science degree.
- Strong familiarity with the Azure data technologies stack and Extract Transform Load (ETL) tools.
- 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 ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management.
- Strong experience working with large, heterogeneous datasets, building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include ETL/ELT, message-oriented data movement, API design and access and upcoming data ingestion and integration technologies such as stream data integration, and data virtualization.
- Strong experience with popular database programming languages including SQL, PL/SQL, others for relational databases and certifications on upcoming NoSQL/Hadoop oriented databases.
- Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as R, Python, Java, C#, and others.
- Strong experience working with message queuing technologies such as Azure Service Bus, stream data integration technologies and stream analytics technologies.
- Strong experience working with DevOps capabilities like version control, automated builds, testing, and release management capabilities using tools like Azure DevOps.
- Basic experience working with data governance/data quality and data security teams and specifically information stewards and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification. Ability to build quick prototypes and translate prototypes into data products and services in a diverse ecosystem.
- Demonstrated success working with large, heterogeneous datasets to extract business value using popular data preparation tools to reduce or even automate parts of the tedious data preparation tasks.
- Experience with Cloud-based data visualization platforms such as PowerBI.
- Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes.
- Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse, and automation of data flows between data managers and consumers across an organization.
- This position is required to work from home
- Some travel across Canada may be required to meet with clients, stakeholders, or off-site personnel/management.
- Full vaccination against COVID-19 is mandatory for this position and operational deployments (the CRC will however adhere to its duty to accommodate those who are unable to be fully vaccinated for a reason related to a human right protected ground).
If you require accommodation measures during any phase of the hiring process, please notify us as soon as possible. All information received in relation to accommodation requests will be kept confidential.