J&M Group, Inc -
250 emplois
Mississauga, ON
Posté hier
Détails de l'emploi :
Temps plein
Niveau d`entrée
1. 6-8 years of BSA experience in data analytics/data warehouse/data integration through agile environment.
2. Strong communication, interpersonal, collaboration, and negotiation skills to work seamlessly in an agile cross-functional team, with clients, vendor(s) and other stakeholders.
3. Creative, innovative, and can think outside the box to visualize solutions that meet business needs.
4. Strong experience in data analysis, profiling, clean-up, data wrangling and validation. Hands-on experience building data quality checks and writing complex SQL queries.
5. Experience working in large scale Enterprise Data Lake ingestion and extraction projects.
6. Experience working with Jira, Confluence, Bitbucket.
7. Experience with Data Lake, Azure Data Factory, DataBricks, python, Synapse/SQL Server, Analytics Zone, Azure Scheduler, Tableau, Power BI.
8. Demonstrable experience with conceptual, logical and physical data modeling techniques.
9. Solid knowledge of metadata management principles, metadata modeling and writing metadata definitions.
10. Good understanding of data Security Classifications, Risk Assessment (TRMIS) guidelines.
2. Strong communication, interpersonal, collaboration, and negotiation skills to work seamlessly in an agile cross-functional team, with clients, vendor(s) and other stakeholders.
3. Creative, innovative, and can think outside the box to visualize solutions that meet business needs.
4. Strong experience in data analysis, profiling, clean-up, data wrangling and validation. Hands-on experience building data quality checks and writing complex SQL queries.
5. Experience working in large scale Enterprise Data Lake ingestion and extraction projects.
6. Experience working with Jira, Confluence, Bitbucket.
7. Experience with Data Lake, Azure Data Factory, DataBricks, python, Synapse/SQL Server, Analytics Zone, Azure Scheduler, Tableau, Power BI.
8. Demonstrable experience with conceptual, logical and physical data modeling techniques.
9. Solid knowledge of metadata management principles, metadata modeling and writing metadata definitions.
10. Good understanding of data Security Classifications, Risk Assessment (TRMIS) guidelines.
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