Role: Teradata Lead
Location: Toronto, Ontario
Onsite
Skill Matrix:
Recruiter Rating (No.of.exp / Beginner / Advanced / Expert)
Deep Teradata architecture
Responsibilities
- Provide leadership and direction for enterprise data strategies; develop strategy presentations, roadmaps, socialize concepts, develop business cases, liaise with teams for planning and implementation
- Provide technical leadership in data management: data modeling and db design, DWE Solution Architecture, Big data Architecture, data integration, data movement and replication, business intelligence, data quality, reference and master data management, data governance etc.
- Provide DW/Big data Solution Architecture expertise
- Provide leadership and direction for integrating and rationalizing data design within Data Management Environment, promoting reuse and quality
- Promote the goals of data integration, data stewardship, data quality, and governance.
- Provide leadership and direction for enterprise data strategies including Strong skills in data management: data modeling and db design, DWE/Big data Solution Architecture, data integration, data movement and replication, business intelligence, data quality, reference and master data management, data governance
Must-have
- Strong skills in data management: data modeling and db design, DWE Solution Architecture, data integration, data movement and replication, business intelligence, data quality, reference and master data management, data governance
- Hands-on implementation experience working with a combination of the following technologies: Hadoop distributions, Storm and Spark streaming, Kafka, Spark advanced analytics, NoSQL data warehouses such as Hbase and Cassandra, data processing frameworks
- Experience in designing and implementing big data solutions. This includes creating the requirements analysis, design of the technical architecture, design of the application design and development, testing, and deployment of the proposed solution
- Expertise in database integration patterns and performance considerations for operational and DWE databases
- Experience with patterns and technologies in emerging areas: Big Data, Hadoop, Hortonworks, Aster, NoSQL databases, Visualization tool, Advanced Analytics, BI Virtualization, Cloud etc.
- Domain Knowledge in financial services platforms and enterprise functions (e.g. Banking, Insurance, Investments, Wealth Management, AML, Risk, Finance, Fraud etc.)
Nice-to-have
- Experience with technologies (Teradata, DB2, SQL Server, Oracle, DataStage, Oracle BI etc.)
- Experience with Hortonworks Data Platform (HDP)
- Experience with Vertica or other MPP databases
- Experience with Java/Python/functional programming
