Develop architectural strategies for data modelling, design and implementation to meet stated requirements for metadata management, master data management, Data warehouses, ETL and ELT.
Analyzing business requirements, designing scalable/ robust data models, documenting conceptual, logical physical data model design, helping developers in development/ creating DB structures and supporting developers throughout the project life cycle.
Lead and Mentor Data Engineers: This role will be responsible for leading and developing a team of data engineers focused on the growth in the teams skills and ability to execute as a team using DevOps and Data Ops principles.
Investigate new technologies, data modelling methods and information management systems to determine which ones should be incorporated onto data architectures, and develop implementation timelines and milestones.
Recognizes and resolves conflicts between models, ensuring that information and data models are consistent with the ecosystem model (e.g., entity names, relationships and definitions).
Participates in the design of the information architecture: supports projects, reviews information elements including models, glossary, flows, data usage.
Provides guidance to the team in achieving the project goals/milestones.
Works independently within broad guidelines and policies, with guidance in only the most complex situations.
Contribute as an expert to multiple delivery teams, defining best practices, building reusable design components, capability building, aligning industry trends and actively engaging with wider data communities.

Skills and Experience

Graduate or post graduate in Computer science/Electronics/Software engineering.
6+ years of relevant experience in Data modelling for DW analytics applications (OLAP) / Database related technologies.
Expert data modelling skills (i.e. conceptual, logical and physical model design, experience with Enterprise Data Warehouses and Data Marts).
Solid understanding of cloud database technologies and services (eg..GCP, Redshift, Aurora, DynamoDB, etc)
Experience in working with data governance, data quality, and data security teams.
Experienced in knowledge-driven data processing techniques like data curation, representation, standardization, normalization and any other type of processing that prepares the data for integration, persistence, analysis, exchange/share and so forth.
Experience in handling very large DBs and large data volumes
Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include ETL/ELT, data replication/CDC, message-oriented data movement and upcoming data ingestion and integration technologies such as stream data integration and data virtualization.
Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production.
Knowledge with popular data discovery, analytics, and BI software tools like MicroStrategy, Tableau, Power BI and others for semantic-layer-based data discovery.
Ability to lead and mentor teams for effective delivery
Crisp and effective executive communication skills, including significant experience presenting cross-functionally and across all levels.

Data Architect