Senior Engineering Manager - Supply & Demand at Kpler – London
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About This Position
We are seeking a Senior Engineering Manager to lead Kpler’s Supply & Demand (S&D) crew within the Commodities Tribe. The team develops the computation engine that delivers real-time global supply and demand balances for oil, LNG, and grains, combining diverse data sources, analyst expertise, and machine learning to support critical trading decisions.
You will be responsible for the end-to-end delivery of a portfolio of 12+ production systems, including data pipelines, ML-driven quality assurance, APIs, and cloud infrastructure (AWS, Kubernetes, Airflow). Your mandate is to build and sustain a high-performing, autonomous team that owns outcomes from data ingestion through to client-facing products.
You will establish and maintain high standards of operational excellence across SLOs, incident management, and continuous improvement, while fostering a strong culture of ownership supported by robust CI/CD and observability practices. The team already operates at an elite level; your focus will be to sustain and extend this performance as the product scope expands.
This role reports to the Tribe Lead of the Commodities Tribe.
Build and sustain an autonomous crew that owns systems end-to-end, from data pipelines to APIs including production operations, incident management, and on-call, while removing organisational blockers to maximise delivery focus.
Champion SLO-driven reliability (via Datadog), with robust runbooks, blameless post-mortems, and a strong emphasis on proactive observability to detect issues before users.
Structure and prioritise work with clear milestones and a consistent delivery cadence, balancing short-term operational needs with longer-term strategic initiatives; use metrics (e.g. velocity, cycle time, effort allocation) to inform decisions and ensure transparency.
Maintain high engineering standards by creating space for testing, automation, observability, documentation, and technical debt reduction; guide architectural decisions across Python and TypeScript systems.
Oversee AWS infrastructure (Terraform-managed) and platform components (ArgoCD, Airflow), ensuring scalability, reliability, and cost efficiency while supporting expansion into new commodity domains.
Develop and retain engineers through clear goals, regular feedback, and career development, fostering high performance while addressing challenges with empathy and clarity.
Partner with recruitment to maintain a strong hiring bar and ensure the team is appropriately staffed and balanced in skills and seniority.
Collaborate closely with Product, Research, and Data teams, communicating progress, managing risks, and increasing the visibility and impact of the crew’s work.
Leverage retrospectives, health checks, and existing metrics to drive continuous improvement, building on an already strong data-driven culture.
Share and scale best practices across the wider engineering organisation, contributing to broader technical excellence and consistency.
Overall 8+ years of engineering management experience, including at least 3 years leading teams of 5+ engineers on production, data-intensive systems.
Proven success building autonomous, high-performing teams that own systems end-to-end—from data ingestion to client-facing APIs.
Experience managing distributed or remote teams across multiple time zones.
Track record of recruiting, developing, and retaining engineering talent, with confidence in performance management and career growth conversations.
Strong experience managing teams that build and operate scalable data pipelines or ETL systems using tools like Airflow, Dagster, or Prefect.
Hands-on background with Python backend systems (FastAPI, pandas/polars, SQLAlchemy), as IC or manager.
Proficient with cloud-native infrastructure on AWS (or equivalent), including RDS/Aurora PostgreSQL, S3, Kubernetes/ECS, IAM, and Terraform.
Familiarity with CI/CD pipelines, GitOps practices, and deployment automation (GitHub Actions, ArgoCD).
Strong operational mindset: SLOs/SLIs, incident management, on-call rotations, and post-mortems.
Experience managing teams building APIs in TypeScript/Node.js (NestJS, Express, GraphQL/Apollo); the crew’s API layer is TypeScript-based.
Familiarity with production ML systems (e.g., Prophet-based anomaly detection for data quality).
Experience in commodities, energy, or fintech domains.
Exposure to Dataiku or similar data science platforms used by analyst teams.
Experience with monorepo architectures and shared library/framework design patterns.
Familiarity with observability tooling (Datadog, Prometheus, Grafana) and API gateway management (Kong).
Experience managing teams serving both internal platform consumers and external API clients (dual-interface API patterns).
Background in organisations using tribe/squad/crew models (Spotify model or similar).