tem

• Set and raise the bar on analytics engineering standards. Define the patterns, testing, and review practices that keep dbt models across the business consistent, documented, and trustworthy without you personally checking every one. • Own the context layer. Bring the semantic layer (Omni) and the underlying domain models together into one place the business, human or AI, can query with confidence. • Build the domain model from first principles. Take tem's data from raw source to a structured, trusted layer that powers commercial, financial, risk, and operational decisions, not just dashboards. • Expand analytics engineering's reach across the business. Integrate new data sources, product and platform events, and the tools other departments run on, taking analytics engineering from a centralised function into new corners of the company. • Help tem think bigger. As the business looks at big bets like international expansion and new ways of bringing its technology to other companies, help build a domain layer that's ready to go with it. • Partner across the business, not just the data team. Work directly with engineers, product managers, and salespeople to understand what they're actually trying to achieve, then turn that into models that hold up under real use.

• Take business and customer needs and turn them into feasible, shipped product, working closely enough to the ML and forecasting work to make good calls on it. • Own the technical-product detail, setting clear success criteria for how well a model or forecast needs to perform, and designing for the edge cases and operational reality of a system carrying live financial risk. • Drive open product decisions to a close, so delivery keeps moving at pace, with a clear Now, Next and Future for the initiatives you own. • Take initiatives from discovery through to regularly released product, with a clear Now, Next and Future for a given area. • Decide what to formalise, what stays a prototype and what to drop - be the editor and builder, not just the instigator. • Work embedded in full-stack teams that include ML/data science and software engineers, close enough to the models to make good calls. • Pull and interrogate your own and others' data to influence and inform decisions, and reason about model performance and error as business indicators. • Find the tangible and most pressing problems worth solving - such as whether to buy or build a new forecasting capability, or how to ensure Rosso reacts at scale to any event in the system.

• Own the delivery for new product categories • Lead technically through execution - design, build, and ship alongside the squad, not just direct it. • Translate ambiguous, commercially-driven problems into a shape the squad can deliver against. • Partner with commercial and product stakeholders to keep new-product work tied to real pipeline outcomes. • Mentor the squad's engineers and raise technical standards, without owning their performance or career progression. • Drive the Origination & New Products squad's technical direction on deep integrations with other internal services • Stay hands-on: design, build, and review the hardest parts of the new-product build sequence yourself. • Take a company target ("grow origination volume through new products") and break it into a sequenced, deliverable plan without needing to be told how. • Design systems for categories that don't exist yet at tem - Unlocking TWh’s of new pipeline - where the spec is being written as you go. • Make judgement calls on commercial risk in deal-gating tooling, not just deliver what's asked. • Prevent domain drift as new product categories are added, keeping shared language and clear boundaries. • Work directly with the Engineering Manager and commercial stakeholders to keep new-product delivery tied to origination pipeline outcomes, not just shipped features. • As Sell Side expands internationally, represent the technical view in conversations with new-market stakeholders and partners. • Mentor engineers in the squad and raise engineering standards, while staying on the individual-contributor track.

• Own capital strategy and sharpen the equity story, including non-equity facilities. • Deal with pre-emptive funding and manage in-bound investor relations: data room, metrics narrative, investor-grade reporting. • Lead tax and Big Four audit preparation. • Own the operational set-up of tem's US entity ahead of our Texas launch - legal, tax, banking, reporting. • Shape revenue strategy and platform growth for the third-party infrastructure model. • Own ERP integration and the flow of financial information to key stakeholders. • Plug into an AI-forward finance set-up - most of the team structuring is covered; you bring the judgement layer. • Leave the function cleaner than you found it: systems, processes, and reporting a permanent CFO can inherit on day one.

• Reset the bar: rethink how tem finds and converts engineers from first principles, and run bold experiments rather than copying other companies' playbooks. • Own speed at senior level: take accountability for time-to-hire on senior and staff roles, and strip out the friction that slows great people down. • Lead employer brand and attraction: build tem's reputation as a place the best engineers want to join, through creative, brand-led campaigns and events. • Own the pipeline: run proactive, passive-first sourcing and creative routes to talent and keep a pipeline ahead of demand. • Own workforce planning: partner with engineering GMs to plan headcount, turn vague needs into sharp specs, and calibrate what good looks like. • Run on data: own hiring metrics end to end, use them to find bottlenecks, prove what works, and keep raising both speed and quality. • Put AI to work: design AI and agent-assisted talent workflows that scale the repeatable and free up human judgement for the calls that matter.

• Build and grow high-performing teams: Lead hiring, onboarding, performance, and progression across both the DevEx and DevOps teams - creating an environment where Staff and Senior Engineers feel supported, challenged, and set up to succeed. • Drive delivery through clarity and alignment: Help both teams prioritise, stay focused, and move quickly. You'll create structure without over-engineering, ensuring everyone knows what matters and how to execute on it. • Champion developer experience: Understand what actually enables engineers to do great work, and use that to guide where DevEx effort goes. This is the area with the most headroom, and where you'll spend the most attention. • Support technical direction: Partner with your Staff and Senior Engineers to guide decision-making, uphold quality, and drive continuous improvement, without stepping on their autonomy or trying to out-expert them. • Stay hands-on: Get into the code where it matters most - support the more complex projects, spot problems early, and keep a feel for the work. • Create strong cross-functional connections: Act as a bridge between DevEx/DevOps and the wider Engineering org, making sure both teams stay focused on what unblocks the most engineers, not just what's technically interesting. • Champion a remote-native culture: Lead with empathy and intention in a fully remote environment, fostering trust and autonomy across two teams, even without regular face time.

• Originate and close deals from first signal to signature, shaping commercials, negotiating terms, and landing contracts at pace. • Source generators, developers and aggregators through targeted, data-led outreach, and run a disciplined pipeline you own. • Build a diversified book across tech type, region, counterparty and tenor to increase liquidity, resilience and pricing depth. • Own commercial diligence and deal hygiene so handoffs to delivery are clean, including key asset data and route-to-market inputs. • Feed structured sell-side signal into pricing, product and strategy to sharpen our proposition and stay ahead of competitors. • Work with Pricing, Legal and Ops to remove onboarding friction and ensure clean handoffs to delivery.

• Work tickets as they come into the payments inbox, keeping response times fast and consistent. • Reach out proactively by email and phone where a customer needs chasing or a payment plan discussed. • Set up payment plans for customers in genuine difficulty, and clearly spell out consequences for those who are able but unwilling to pay. • Handle manual, currently unautomated tasks that support the wider payments process. • Respond to tickets in the payments inbox, working towards a sub-1-hour first response time. • Make outbound calls and send outbound emails to chase unpaid invoices or follow up on open cases. • Set up and manage payment plans for customers experiencing financial difficulty. • Clearly and fairly communicate the consequences of continued non-payment where required. • Navigate multiple internal systems at once to build a full picture of a customer's account and history before engaging. • Complete manual data entry and process tasks currently done by hand, accurately and on time. • Use AI tools where useful to work through volume more effectively without losing quality. • Flag friction, inefficiencies, or repeat issues to the permanent team as they're spotted. • Suggest better ways of handling recurring situations, even where you're not the one implementing the fix.

• Own the technical direction for pricing ML. Define what to build and how. Set the roadmap for the pricing engine as a core piece of tem's IP — and be accountable for its performance. • Formulate and solve the pricing problem properly. The mathematical foundation doesn't fully exist yet. Your first job is to define it: a dynamic, real-time system that simultaneously optimises for signing probability, portfolio balance, and margin. Choose the right approach — stochastic programming, reinforcement learning, classical ML, or a hybrid — based on the problem, not familiarity. • Build and ship models end-to-end. Own the modelling and data layer. Write production-grade Python. Architect models with deployment in mind and carry them through to production — you can execute without being blocked by engineering dependencies. • Solve imbalance problems. Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. • Be the voice of pricing ML across the business. Commercial, product, and engineering teams depend on this engine. They need to understand what it's doing and why. You make that happen — clearly, without losing precision.

• Own and ship complex deliveries. Lead high-impact improvements across Rosso's pricing and deal-flow workflows — improving correctness, operability, and scalability as volume and complexity grow. You drive these from design through to production. • Make architectural decisions that stick. Design and evolve a stateful, event-driven system where domain logic is the primary source of complexity. Create clear domain boundaries and prevent the kind of drift that compounds over time. • Partner with ML and Data Science. Work closely with our ML and Data Science teams on integrating forecasting, optimisation, and pricing models into Rosso's core workflows. You don't need to be an ML practitioner — but you'll be a genuine technical partner in how models land in production. • Lead across teams. Drive cross-functional initiatives spanning engineering, product, data, and commercial stakeholders. Align people around a technical direction and deliver. • Raise the bar. Mentor Staff and Senior Engineers, strengthen observability and testing practices, and act as a trusted technical voice to engineering leadership. • Plan for scale. Architect systems for high-volume quoting activity and large customer counts — anticipating bottlenecks before they emerge.