Lead Data Engineer (SVP)
Working closely with the COO 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
Translating the executive management requirements for all key roles to be filled, into detailed job descriptions and technical requirements
Management of the recruitment pipeline, including details of preliminary vetting of the candidates together with such partners as recruitment agencies and search firms.
Facilitating technology landscape research, and supporting the development of Data Engineering and Automation and expertise in building automated Data Pipelines, within the local ecosystem.
Taking ownership for defining and implementing the full Agile process, tools, and protocols, including documentation processes, as well as the product standards to be followed within the GIC for all project and program work within their remit, using modern tools, team structures, and technology.
Until such time as an independent architecture function is installed, this role will also ensure that an architecture discipline and standards are established and documented to support the work of the engineers and product professionals at the center, in collaboration with the CEO and COO.
The role will also oversee adherence to the broad regulatory and compliance requirements, as well as any government mandates, with all due diligence, and ensure the documentation necessary to support these functions is always in place, in collaboration with the CEO and COO
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)