FP Markets
Financial Services
SeniorAnalyticsEngineer
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“Senior Analytics Engineer at FP Markets. Skills: SQL, dbt, Superset, ClickHouse. Own the full delivery cycle. Collect requirements”
What You'll Achieve.
Build and scaling our new data platform; Shape the analytics foundation; Push production-ready dashboards
Industry & Context.
Investigate source systems; Comfort with ambiguity
What They're Looking For.
Must Have
Strong SQL, dbt production experience, Superset production experience, Python read and modify, ClickHouse, AI-first workflow
Nice to Have
Fintech domain knowledge, Airflow read and modify, Power BI, DAX, Knowledge of CySEC / MiFID II / GDPR / DORA, Open-source data stack experience
What You'll Do.
Own the full delivery cycle
Investigate source systems
Specify upstream needs
Build Superset dashboards
Stakeholder acceptance
Use AI-assisted development workflows
How You'll Work.
Team & Collaboration
Work with a senior team; Direct access to business stakeholders; Direct stakeholder interaction
Communication Scope
Clear written communication; Direct stakeholder interaction
Full Job Description
**FP Markets Group of Companies** is a well-established multi-regulated broker, founded in Australia, offering traders access to CFD trading on Forex, Indices, Commodities, Stocks and Cryptocurrencies. We are growing and looking to recruit a Full-time **Senior Analytics Engineer** in Cyprus office - a certified Great Place to Work®. As a Senior Analytics Engineer, you will play a key role in building and scaling our new data platform from the ground up. You’ll work with a clearly defined modern stack — ClickHouse, dbt, Airflow, Superset — in a self-hosted, on-prem environment, alongside a senior team with direct access to business stakeholders and AI tooling support. The platform direction and priorities are already set, while you still have the authority and ownership to shape the analytics foundation and push production-ready dashboards with confidence. **Reporting to:** Head of Data ### Responsibilities: * Own **the full delivery cycle** for each business request * **Collect requirements** directly from business stakeholders — Finance, Risk, Operations, Product, Business Development. Translate vague asks into precise, testable specifications * **Investigate source systems** even when documentation is missing or incomplete — read code, query raw tables, talk to system owners * **Specify upstream needs** for the Data Engineer when new sources or pipelines are required. Clear, well-formed tickets — not "please get me the trading data." * **Build dbt models** from raw / staging into marts. Own the semantic layer, the tests, the documentation, the lineage * **Build Superset dashboards** that business users actually use. RLS, RBAC, semantic layer in Superset — all configured by you * **Validate the numbers.** Every chart has a known-good ground truth. You can defend any number on any dashboard, with reasoning and reproducible queries * **Document the work** in a way that lets the next person continue from where you stopped — knowledge stays inside the team * **Stakehol
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