AI Native Data Analyst

Work Setup: Hybrid 2-3x a month in Makati

Work schedule: Mid Shift

About the role

As the company's data volume and complexity continue to grow, this role will be responsible not only for validating and improving data quality but also for uncovering meaningful insights, patterns, anomalies, and opportunities that may not be immediately visible. This is not a traditional reporting or dashboard-focused role. The successful candidate will combine strong technical data skills with analytical curiosity and critical thinking. As a solo contributor reporting directly to the CTO in Finland, you will have significant autonomy and the opportunity to define data quality standards, processes, and the overall analytical approach from the ground up.

Key responsibilities

• Ensure the accuracy, consistency, completeness, and reliability of data generated by company tools and platforms.

• Design and implement data validation and quality assurance processes.

• Develop queries and validation logic to identify inconsistencies, errors, missing data, and anomalies.

• Investigate the root causes of data quality issues and communicate findings clearly.

• Establish standards and processes for maintaining trustworthy data.

• Analyze datasets to identify trends, patterns, anomalies, and opportunities.

• Go beyond surface-level reporting to uncover insights that may not have been specifically requested.

• Explore open-ended datasets and develop meaningful analytical questions.

• Apply statistical and analytical techniques to identify outliers, drift, and potential data degradation.

• Translate complex findings into clear and actionable insights for technical and non-technical stakeholders.

• Take ownership of the analytics and data quality function with minimal supervision.

• Define and improve analytical workflows, documentation, and validation processes.

• Work independently and prioritize tasks effectively across a remote, asynchronous environment.

• Collaborate directly with the CTO and senior team members across different time zones.

About you

• 3–6 years of professional experience in Data Analytics, Data Quality, Analytics Engineering, or a related field.

• Degree in Statistics, Mathematics, Computer Science, Economics, or equivalent practical experience.

• Advanced SQL proficiency including window functions, Common Table Expressions (CTEs), data validation queries, reconciliation logic, and complex data investigation and analysis.

• Proficiency in Python or R for data analysis, anomaly detection, data cleaning, and scripted validation pipelines.

• Experience designing, implementing, or auditing data quality frameworks with strong understanding of completeness, accuracy, consistency, and timeliness.

• Experience with at least one BI or data exploration tool such as Tableau, Power BI, Looker, Metabase, or similar analytics platforms.

• Comfortable owning a domain or function end-to-end with minimal oversight.

• Experience working in SaaS, analytics platforms, data products, fintech, or similar environments is preferred.

• Experience in environments where data is a core product rather than simply a supporting function is highly desirable.

AI-Native Mindset

The successful candidate should demonstrate:

• A strong interest in adopting new AI tools and technologies.

• The ability to evaluate when AI can improve speed, quality, or efficiency.

• Good judgment around validating AI-generated outputs.

• An understanding that AI-generated results should be reviewed rather than blindly trusted.

• Curiosity to experiment with new AI tools, workflows, and approaches.

• The ability to combine domain expertise, analytical thinking, and AI-assisted productivity.