almost 2 years ago
|Targeted start date: 2/25/2019
Targeted end date: 2/25/2020
- Or Mandate Duration: 12 months - with opportunity of renewal
Position's country: Canada
Position's city: Montreal
Potential to convert to permanent?: No
Position title: Data Governance Specialist - Senior - Data Quality Specialist
****We will focus on people with experience in ETL, data cleansing, data profiling and it would be great if they have informatica experience (this is definitely a plus)****
- 3-5 years understanding, designing and implementing data quality standards that enable well-integrated transactional, collaborative and analytical systems
- Experience with various data profiling and data quality assessment techniques
- Experience with various aspects of Information Management at the enterprise level (data governance, data quality, master data management, information life cycle, etc.)
- 5-7 of Experience with business intelligence, data warehousing, relational database and other data structures
- Experience with data mapping, data flows and data cleansing
- Experience with TOAD or similar database management tool
- Experience with Qlik View or similar data visualization tool
- Experience with Informatica Data Quality Analyst or similar data profiling tool
- Experience with Informatica Data Quality Developer or similar ETL tool (desirable)
- Experience with Informatica MDM or similar MDM tool (desirable)
- Experienced in business requirements gathering
- Ability to deliver mandate with great autonomy – self-driven
- Excellent written and oral skills
- Motivated by long term results
- Quick and motivated learner
The discipline of data quality management ensures that data is "fit for purpose" in the context of existing business operations, analytics and digital business scenarios. The purpose of this role is to be responsible for the implementation of data quality at CN in the context of a project or support to a business area, involving multiple tasks for data discovery, data improvement and data quality monitoring using the best tools in the market.
Summary of Tasks
- Data Profiling: Being able to analyze data and elaborate a technical data quality assessment
- Data Improvement: Being able to design data quality rules for improvement of data and work with the implementation teams
- Data Monitoring: Being able to design and elaborate data quality monitoring metrics, profiles, rules and dashboards
Data Quality Management at CN involves the tasks for data discovery, data improvement and data quality monitoring using the best tools in the market. In this role your will execute the following activities:
- Profile data sets making use of data visualization, quality and sql tools.
- Identify and document data quality issues.
- Prepare a data quality assessment with recommendations for high quality data.
- Work together with data governance to elaborate a data readiness assessment.
- Work together with information architects to elaborate source-target data mappings and data migration strategies.
- Work together with solution architects to analyze and document data flows and integration specifications.
Data Improvement: Data Quality Rules Definition and Enforcement:
- Design of data quality rules using as inputs data defects, business rules and data governance policies.
- Maintain the enterprise data quality rules catalogue.
- Work together with business leads/users to identify and validate data quality requirements are complete, consistent and achievable; and ensure decisions are documented.
- Work together with data governance to define the enforcement of data quality rules within the organization.
- Work together with internal and external software delivery teams to implement and deploy DQR
Data Quality Monitoring:
- Design of data quality metrics and scorecards.
- Design and build data quality monitoring dashboards.
- Work together with data governance to define monitoring frequency and ownership.
- Participate in data defects root-cause analysis with business users and data governance.
- Coordinate with the project team and members of other data practices (IA, DG, BI) to clarify the tasks, scope and deliverables
- Support the presentation the data quality practice to Project Sponsors and other stakeholders
- Coach junior team members