Enterprise Technology Engineer

Entity:

Technology

Job Family Group:

IT&S Group

Job Description:

We are looking for a Senior Dataiku Product Engineer to work within our digital Trading Analytics Dev Ops team that supports, maintains and improves the Dataiku platform and wider PowerBI and Plotly ecosystem for the Trading Analytics and Insights (TA&I) organisation within the Trading and Shipping (T&S) Entity. You will have a deep understanding of the Dataiku Data Science Studio platform and act as the champion for its use within commodity trading decision support.

Operating at true enterprise scale, supporting hundreds of end users across multiple environments for many different discipline communities.

• Working as part of an agile Dev Ops team you will provide thought leadership on all aspects of platform operations and capability delivery. You will own the technical platform architecture.
• Define key platform and user metrics which will be monitored to pre-empt issues, manage cost and mitigate problem reoccurrence ensuring safe and reliable operations.
• Work with platform Architects and Product Owners to assess new platform capabilities, producing formal assessment and design documentation considering enterprise level requirements and impact on Total Cost of Ownership (TCO).
• Work with Product Engineers to deliver high quality platform solutions to plan through the full lifecycle (design, development, test, implementation).
• Collaborate with the Project Manager / Scrum Lead to estimate and plan deliverables and track execution through the daily standup.
• Collaborate with the Service Delivery Manager to define operational process, practices and standards, supporting major service incidents (P1/P2), subsequently contributing to their Root Cause Analysis (RCA) process.
• Collaborate with TA&I Sponsors, dTA Product Managers and Architects to define the platform product roadmap for both functional and technical capabilities.
• Collaborate with the dTA Product Owner, Scrum Lead and Service Delivery lead to manage the TCO with particular focus on cloud cost optimisation.
• Manage the supplier relationship with Dataiku, including influencing the DSS product roadmap to meet TA&I requirements.
• Define protocol standards for the platform, covering version upgrades, capability roadmap, deployment and hosting, availability/recovery, operations. You will be a standard bearer for quality, reliability, repeatability and cost efficiency.
• Define and apply rigorously repeatable engineering principles through the use of automation for all platform processes; ensuring that all configuration is maintained under source control with automated deployment and test.
• Embody the spirit of continuous improvement through agile sprint retros, RCAs, design review workshops, metrics monitoring forums; coaching and developing junior team members; using data to drive where we invest in the platform automation suite.

• Bachelor or equivalent experience in Computer Science, Engineering, Information Systems or other numerate field.
• Demonstrable experience as a Dataiku Data Science Studio Lead Product Engineer responsible for an enterprise DSS implementation with hundreds of users and multiple environments.
• Deep technical and functional expertise across the DSS product: both its use as a Data Science / Data Engineering platform and of its configuration and automation via APIs.
• Experience of defining, crafting and leading the implementation of enterprise-strength platform capabilities: patching/upgrade strategy, regression testing, availability/recovery (RPO/RTO), RBAC security model, LDAP integration, scalability of data and compute for the DSS platform.
• Deep understanding of enterprise use of python for platform automation and pandas for data engineering. Including defining protocol standards and monitoring for anti-patterns.
• Advanced use of SQL for data engineering and reporting including data modelling and design skills and exposure to ETL principles and practices. Has defined standards & guidelines.
• Definition and design of the full SDLC (standards and tooling): development, test, deployment; using git source control and Jenkins (or ADO) pipelines.
• Use of Azure Dev Ops (or Jira) for agile working practices and ceremonies (standups, sprint planning, retros). Refinement of operational and capability backlogs.
• Knowledge management through ADO task updates, formal documents (Excel, Word) in SharePoint and self-service style wiki pages. .
• Evidence of thought leadership through production of formal designs and implementation of those designs by others.
• Production of architecture results: technical architecture diagrams, Key design decision documents.
• Experience of mentoring and coaching of junior staff through direct review of results and informal technical coaching.

• Dataiku Data Science Studio (DSS)
• Python
• Pandas
• SQL
• ETL principles and practices
• Git
• Jenkins
• Azure Dev Ops (ADO)
• Jira
• AWS (EC2, EBS, RDS, S3, EKS)
• Terraform
• Ansible
• CI/CD deployment and…