Data Analyst (Operations Analytics)

We are looking for an Operations Analytics Analyst to join the Operations Analytics team. This is a hands-on role for someone who is strong in Python and SQL, comfortable working with operational data, and excited by the opportunity to build practical analytics and automation solutions.

You will work on a mix of reporting, deep-dive analysis, automation, and product expansion projects. The role is particularly focused on supporting growing products, including US expansion, by setting up reporting baselines, identifying operational inefficiencies, and building proof-of-concept automations that reduce cost-to-serve and improve scalability.

This is a great opportunity for someone who enjoys context-switching between analytical problem-solving, stakeholder management, and hands-on technical delivery.

What You’ll Do

• Build reporting baselines and performance dashboards for new and growing products, including US expansion.

• Analyse operational workflows to identify bottlenecks, inefficiencies, and low-hanging opportunities for improvement.

• Use Python and SQL to investigate operational performance, cost-to-serve, customer outcomes, and commercial impact.

• Create proof-of-concept automations using Python, APIs, and LLMs to reduce manual work and improve decision-making.

• Support analysis across areas such as QA, disputes, AML, fraud, customer support, vulnerability, workforce planning, and service operations.

• Translate ambiguous operational problems into clear analytical questions, outputs, and recommendations.

• Work closely with Operations, Product, Data, and senior stakeholders to prioritise and deliver high-impact work.

• Support data quality, metric definition, and reporting consistency as new products and processes scale.

• Present findings clearly to both technical and non-technical stakeholders.

What We’re Looking For

• Minimum 1 year of experience in an analytics, data, operations, or technical role.

• Strong Python skills are essential, including experience with data analysis, automation, and working with structured datasets.

• Strong SQL skills, with the ability to query, join, transform, and analyse large datasets.

• Good understanding of basic statistics, including distributions, averages, variance, conversion rates, confidence, and trend analysis.

• Basic understanding of data science principles, such as classification, prediction, model evaluation, and feature thinking.

• Strong analytical problem-solving skills and the ability to move from problem definition to insight and recommendation.

• Comfortable working in ambiguous, fast-paced environments where priorities can change.

• Able to operate as both a hands-on analyst and a pseudo-PM when required.

• Strong communication skills, with the ability to explain analysis clearly to senior stakeholders.

• Comfortable context-switching across reporting, analysis, automation, stakeholder questions, and product support.

Nice to Have

• Experience with dbt or modern analytics engineering workflows.

• Experience building or maintaining data pipelines.

• Experience integrating with REST APIs.

• Exposure to LLMs, prompt engineering, AI automation, or AI engineering workflows.

• Experience building end-to-end Python automations or internal tools.

• Understanding of operational workflows such as QA, fraud, disputes, AML, IVR, workforce planning, or customer support.

• Experience working with product teams or supporting new product launches.

Interview process

• Screening Call + Python Questions

• Live Technical Python Interview

• Case Study Interview

• Final culture-add interviews