Engelhart was founded in 2013 by BTG Pactual Group as a commodities trading company. Our business model is "asset light" and highly diversified – giving us the ability to adapt effectively and nimbly to changing market conditions. We have assembled successful multidisciplinary teams, leveraging advanced fundamental analysis with deep quantitative and weather research capabilities. Our activities are underpinned by strong risk management practices and by powerful technology and operational excellence. We have exceptional teams with diverse global backgrounds and decades of experience, and are driven by a highly collaborative culture, across products and competencies.
In 2024, Engelhart acquired Trailstone, a global energy trading and technology company. The acquisition provides us with new expertise, analytics and proprietary technology which is being used to provide risk management and optimisation services to help maximise the value of our clients' renewable power. The acquisition also expanded Engelhart's capabilities into physical natural gas across North America, a critical fuel to support the energy transition.
Our talented and experienced individuals work together according to its four company values: Performance, Agility, Collaboration, Entrepreneurship.
As a Technology Manager in our Tech Data, AI & UX team, you will lead a high-impact engineering group responsible for building, scaling, and operationalising data and AI capabilities across Engelhart's trading, quantitative, risk and operational functions. This is a hands-on leadership role at the intersection of data engineering, AI platform delivery, front-office technology, and commodities trading
The role reports to the Global Head of Tech - AI, Data & UX and will play a central part in implementing Engelhart's AI Business Plan. That plan positions AI as a federated capability enabled by a central Tech AI team with responsibilities across platform, governance and delivery, working closely with trading desks, Quant & Systematic teams, MOBO, Risk, Compliance, People, senior management, external AI providers, cloud providers, data providers, BTG Pactual, Gridfuse, Metdesk and other partners.
This role will work directly with Traders, Quants, FO Analysts, Risk, Compliance and Technology stakeholders. Experience with systematic trading, quantitative research workflows, or trading analytics platforms is highly desirable. The successful candidate will help translate front-office needs into robust technical capabilities, ensuring that AI and data solutions are useful, trusted, governed and adopted in real workflows.
The broader mandate is to help Engelhart move from experimentation to production-grade AI capability: improving productivity, making company knowledge searchable, enabling AI-assisted workflows, and embedding AI into analytics through initiatives such as Risk Copilot, Treasury Copilot, Power Forecast Copilot, Trader Copilot, and Quant Copilot.
You will manage a team consisting of four Senior Data Engineers and two AI & Agentic Platform Interns, guiding delivery across both the core data engineering estate and the emerging agentic AI platform. The Senior Data Engineers are focused on building and maintaining data pipelines, integrating and cleansing data, supporting analytical and operational use cases, and enabling predictive analytics, machine learning, data mining, and systematic trading initiatives. The AI & Agentic Platform Interns support the design and implementation of Engelhart's central Agentic AI platform, including orchestration layers, agent runtimes, AWS Lambda integrations, MCP servers, agent skills, knowledge bases, vector databases, retrieval pipelines, and AI productivity tooling.
This will be a full-time role based in our London office and owning the following responsibilities:
- Lead and develop the AI & Data engineering team, directly managing four Senior Data Engineers and two AI & Agentic Platform Interns, setting clear priorities, coaching technical growth, reviewing delivery quality, and ensuring the team operates with high ownership, collaboration, and continuous improvement.
- Drive implementation of the Agentic AI platform, supporting orchestration layers, agent runtimes, AWS-hosted services, Lambda-based tooling, MCP servers, knowledge bases, vector databases, retrieval pipelines, agent skills and secure deployment patterns within Engelhart's internal network.
- Support implementation of the AI Business Plan, helping deliver the central Tech AI team's mandate across platform, governance and delivery, and contributing to the strategic themes of productivity, knowledge and data discovery, AI-assisted workflows, and AI-assisted analytics.
- Support delivery of AI use cases such as Trader Copilot, Quant Copilot, Risk Copilot, Treasury Copilot, Power Forecast Copilot, reconciliation automation, AI Explorer, text-to-SQL, Confluence/wiki agents, contract/vendor agents and self-service AI workflows where relevant to the agreed roadmap.
- Own delivery of scalable data engineering capabilities across Engelhart's data landscape, including ingestion of structured and unstructured data, data cleansing, enrichment, transformation, storage, distribution, visualisation and operational reuse for Data Scientists, Traders, Quant developers and business stakeholders.
- Partner with Quant and Systematic teams to enable data, AI and engineering capabilities that support research workflows, model-adjacent analytics, signal review, back testing support, code review, documentation and systematic trading productivity, while respecting ownership boundaries with Quant and trading teams.
- Translate front-office requirements into practical technology solutions, working closely with Traders, Quants, FO Analysts, Risk and other business teams to identify high-value opportunities.
- Champion production-grade engineering standards, including robust architecture, secure data access, maintainable code, CI/CD, infrastructure-as-code, observability, documentation, testing and clear operational ownership.
- Ensure AI solutions are governed appropriately, contributing to standards around evaluation, provenance, human-in-the-loop design, auditability, monitoring, access control and safe deployment in collaboration with Compliance, Risk, AI Security and senior Technology stakeholders.
- Measure and communicate value delivered, helping connect technical delivery to adoption, cost savings, front-office efficiency and business impact. The AI Business Plan identifies annualised value delivered and value-weighted adoption as key success measures.
This individual will be an experienced engineering manager from a commodities trading environment. Someone who is comfortable leading a team of senior engineers whilst remaining close enough to the architecture and code to challenge design decisions, unblock delivery and set a high technical bar. The successful individual will understand that AI adoption is not just about models or tools; it depends on data quality, platform reliability, governance, user trust and day-to-day workflow fit.
We believe the following background, experiences and skills are essential for application:
- Strong domain understanding of commodities trading is essential.
- Academic background or equivalent professional experience in Computer Science, Engineering, Data Science, Information Systems, Quantitative Finance, Mathematics or a related technical field.
- Significant experience leading data engineering, AI engineering, platform engineering or technology delivery teams in a demanding business environment.
- Proven experience managing, coaching and developing engineers; including senior individual contributors and early-career technical talent.
- Strong hands-on understanding of modern data engineering practices, including data ingestion, transformation, cleansing, storage, distribution, data quality, documentation and operational support.
- Advanced proficiency in Python and the wider Python data ecosystem, including experience with Pandas or comparable libraries for data crawling, parsing, analysis and engineering workflows.
- Experience with analytical, time-series, or large-scale data platforms. ClickHouse is preferred and Redshift is also relevant, based on the current technical landscape.
- Strong experience with cloud-native engineering, preferably AWS, including practical understanding of services such as S3, Lambda, Athena, EMR, Fargate, Kinesis, EC2, API Gateway, CloudWatch, and related architecture and security patterns.
- Hands-on experience with Docker, Git, CI/CD, infrastructure-as-code, and modern software delivery practices.
- Practical understanding of AI, LLM, or Generative AI application architecture, including RAG, knowledge bases, vector databases, prompt engineering, evaluation, orchestration, agent workflows, or AI-assisted developer tooling.
- Experience engaging directly with Traders, Quant developers, Data Scientists, analysts or front-office stakeholders to gather requirements, challenge assumptions and deliver useful technical solutions.
- Strong understanding of commodities trading, particularly Oil, Power, and Gas; including the role of market data, analytics, risk, weather, fundamentals, trading workflows and front-office decision support.
- Ability to translate complex technical concepts for both technical and non-technical audiences, influencing decisions and building trust with stakeholders across levels.
- Strong organisational skills and a strong sense of ownership, accountability, and follow-through. Preferably with a bias toward delivering production-grade solutions rather than prototypes that do not land.
In addition, the following experiences and skills are not required, but highly desirable for this role:
- Commodities coverage: Oil, Power, and Gas.
- Experience in systematic trading, quantitative research platforms, trading analytics, signal generation, back testing workflows, or model-adjacent engineering.
- Practical exposure to agentic AI frameworks and implementation patterns, such as LangChain, n8n, MCP servers, AWS Bedrock, Postgres with pgvector, Bedrock Knowledge Bases, Pinecone, Chroma, or comparable tools.
- Experience designing or operating secure internal AI platforms, including private model deployment, VPC-based architectures, data-access controls, auditability, monitoring, and vendor due diligence.
- Familiarity with AI governance, evaluation suites, provenance, human-in-the-loop workflows, risk classification, model monitoring, audit trails, and compliance considerations such as the EU AI Act.
- Experience with REST APIs, SSIS, Entity Framework, internal API design, Python packages, and integration patterns across enterprise systems.
- Experience with big data technologies such as Apache Spark, Databricks, Parquet, or Hive.
- Experience with data visualisation libraries or platforms, including Plotly or equivalent tooling.
- Experience working with GitHub Copilot-style engineering workflows, internal AI chat, summarization pipelines, or AI productivity tooling.
- Experience working in a federated technology model where a central platform team enables business-aligned teams to own and improve their own workflows.
- Experience engaging with external data vendors, cloud providers, consulting partners, or group-level technology partners.
- Competitive compensation and participation in Engelhart's discretionary bonus plan.
- 25 days of annual holiday entitlement, excluding UK public holidays.
- Robust benefits package such as medical, dental, life insurance, generous pension contribution, and supplemental benefits partially subsidised by the Company.
- Eligibility to receive external and internal training in accordance with our Training & Development Policy.
We believe in inclusivity and are therefore dedicated to ensuring all employees – across gender identity, race, ethnicity, sexual orientation, religion, life experience, background and more – feel welcome and included in the company. We promote diversity because we believe it is essential to our ability to think holistically.
