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数据分析师岗位，负责将业务问题转化为数据分析，构建和维护数据仓库中的数据模型与转换，实施数据质量检查，构建仪表盘和报告，与产品、运营等多部门合作，推动自动化和AI辅助分析。
Turn complex business questions into clear, actionable analysis, identifying trends, opportunities, risks, and the key drivers behind business performance. Work directly with the people who have the data need to understand their questions, challenge assumptions, and turn raw data into meaningful insights and decisions.
Data Foundations: Build and maintain the data transformations needed to turn raw data into clean, reliable, analysis-ready datasets in the Data Warehouse. Work hands-on with imperfect data, validate its accuracy and meaning, and identify gaps or inconsistencies in how data is captured.
Data Modelling: Translate business requirements into well-structured, reusable data models and clearly defined metrics that provide a trusted source of truth for analytics across the organisation.
Data Quality: Implement and maintain data quality checks and tests to ensure data is accurate, consistent, complete, and fresh - proactively identifying and resolving issues with relevant stakeholders and working with Engineering to improve how underlying data is captured.
Reporting &
Self-Service: Build and support dashboards, reports, and analytical datasets that are intuitive, performant, and optimised for internal reporting and self-service analytics. Help turn recurring analytical needs and ad-hoc analysis into stable, scalable, and increasingly automated reporting.
Stakeholder Partnership: Work closely with Product, Operations, Finance, Marketing, Engineering, and other teams to understand business needs, challenge assumptions, and translate them into meaningful analytical questions and solutions. Communicate effectively across different levels and functions, from discussing detailed data questions with technical teams to presenting clear insights and recommendations to senior stakeholders.
Documentation &
Standards: Maintain clear documentation for datasets, transformations, metrics, and data lineage using dbt Docs or similar tools, and contribute to practical standards for SQL, data modelling, testing, and visualisation.
Automation &
AI: Identify manual or inefficient analytical workflows and improve them through automation, modern data tooling, and AI-assisted analysis - using AI to accelerate data exploration, querying, documentation, and reporting while maintaining a strong understanding of and control over the underlying data.
3+ years of hands-on experience in Data Analytics, Product Analytics, Business Analytics, or a closely related analytical role, with a track record of turning complex business questions and raw data into reliable analysis, actionable insights, and business decisions
Strong analytical and statistical thinking, with the ability to identify trends, anomalies, and root causes, define meaningful metrics, challenge assumptions, and work confidently with imperfect or incomplete data
Independent, structured, and pragmatic, with a strong sense of ownership. You are equally comfortable working with business stakeholders and engineers, communicate clearly across different levels and functions, and can bridge the gap between business needs and technical solutions. You are also genuinely curious about AI-assisted analytics and modern cloud data workflows, while maintaining full control of and understanding of the underlying data
Strong business understanding and analytical curiosity, with the ability to quickly grasp the essentials of a complex, finance-driven business, understand how the underlying processes work, and translate business needs into meaningful analytical questions. Experience with financials, commodity trading, finance analytics, or accounting is a strong advantage
Strong SQL skills with experience working with complex datasets, data transformations, joins, aggregations, window functions, and analytical data models
Proven experience working with modern Data Warehouses such as Snowflake, BigQuery, Redshift, Databricks, or similar, including building and maintaining clean, reliable datasets for analysis and reporting
Experience with modern analytics engineering practices and tools such as dbt, including data modelling, automated data quality tests, documentation, and data lineage
Strong experience with BI and visualisation tools (e.g. Metabase, Looker, Tableau, Power BI or similar), with a focus on building clear, performant dashboards and enabling self-service analytics
