Senior Data Engineer (VP)
The successful candidate would have direct, relevant experience in Data Engineering and Automated Data Pipelines for consumption by large-scale AI/ML programs, inclusive of product/solutions/systems design, build, and deployment experience in global settings. The candidate would have demonstrated excellence in managing highly skilled and trained staff delivering Data Automation Product, and excellence in managing high quality product delivery practices. In addition to these technical skills, the candidate would need to demonstrate leadership, teamwork, client alignment, and industry acumen throughout their career history.
Planning and execution of the operational roll-out of GIC core delivery capabilities
Creating favorable conditions for staff under their management to maintain a high level of training and readiness in the latest approaches, architecture, and generally state-of-the-art in AI and ML product development, focusing specifically on Data Engineering, Pipeline Automation, and Pipeline Management
Assisting the Senior Staff and the COO of GIC for all deliverable tracking functions in the Data Engineering and Data Automation portfolio: Tracking and Reporting processes covering KPIs, including deliverables and project/program updates, budget, and risks/mitigations for use by the CEO and other governance bodies
Playing a key role in implementing the Agile process, tools, and protocols, including documentation processes, as well as product standards within the GIC for all project and program work within their remit, using modern tools, team structures, and technology
Cloud Services: AWS (Sagemaker, Lake Formation, S3, EC2, etc.), Google Cloud, Azure Services, AWS Services – S3, AWS SageMaker
Databases: MySQL, MS SQL, Oracle, Neo4J
Data Automation: Lake Formation, Palantir Foundry, Spark
Data Analytics Tools: Power BI, Palantir Foundry, HubSpot, Google Analytics
Tools and Environments: JIRA, Bitbucket, Confluence, Palantir Foundry, Jupyter Notebook, MLFlow .
Tech Practice and Expertise:
Understanding of process for Algo and Model Prototyping, Construction, and Validation using Agile approach
Understanding of Supervised and Unsupervised ML paradigms
Exposure to Core Frameworks (Tensorflow, Caffe, PyTorch, SparkMLlib, etc.)
Exposure AI ML Platform Services including AutoML (AWS Sagemaker, AutoPilot)