Associate Analyst, Analytics & Metrics

We are seeking a Data Analyst to transform complex payments, transaction, customer, and operational data into accurate reporting and actionable business insights. The role combines hands-on data management, data quality, SQL and Python analysis, dashboard development, reporting automation, and stakeholder collaboration.

Responsibilities

• Data Preparation, Quality and Governance:
• Extract, compile, cleanse, validate, standardize, and organize large datasets from multiple internal and external sources using SQL, Python, Excel, and approved data platforms.
• Review data for completeness, accuracy, consistency, duplication, and compatibility; investigate discrepancies and correct issues where possible or escalate them through defined channels.
• Research and obtain missing information required to complete datasets, records, or supporting documentation.
• Apply defined data extraction, labelling, categorization, mapping, reconciliation, and quality-control procedures.
• Perform internal quality reviews and maintain clear evidence of checks, corrections, assumptions, and data lineage.
• Handle sensitive and confidential information with professional maturity and comply with applicable data privacy, security, governance, and retention requirements.

• Analysis and Business Insights:
• Analyze payments and transaction data to identify trends, anomalies, customer behavior, operational performance, and potential growth or efficiency opportunities.
• Use descriptive statistics, trend analysis, segmentation, model testing, and root-cause analysis.
• Translate business requirements into structured analytical approaches, datasets, metrics, and reporting solutions.
• Interpret findings with the end user in mind and provide clear, practical recommendations supported by data.
• Collaborate with business, operations, product, risk, and technology stakeholders to clarify requirements and validate analytical outputs.

• SQL, Python, Databricks and Automation:
• Write and optimize SQL queries using joins, common table expressions, subqueries, aggregations, and window functions for extraction, validation, reconciliation, and analysis.
• Use Python, particularly pandas and NumPy, to manipulate data, perform quality checks, conduct analysis, and build reusable processing scripts.
• Work with Databricks notebooks and Spark or PySpark for data preparation and distributed processing, with guidance for more complex engineering activities.
• Automate recurring reports, validation routines, and data preparation processes to reduce manual effort and improve consistency.
• Support basic machine-learning activities such as feature preparation, model testing, performance evaluation, classification, clustering, or predictive analysis under established frameworks.
• Document code, logic, dependencies, business rules, and operating procedures so solutions can be reviewed, maintained, and reused.

• Reporting and Visualization:
• Build, maintain, and enhance dashboards in Power BI, Tableau, or similar tools to track business metrics, KPIs, trends, and exceptions.
• Prepare accurate recurring and ad-hoc reports, presentations, and analytical summaries for technical and non-technical audiences.
• Validate dashboard calculations and reporting outputs against source systems before release.
• Store completed work in designated repositories, maintain appropriate versions, and perform backup activities as instructed.
• Present findings, limitations, and recommendations clearly to stakeholders and respond to follow-up questions with supporting evidence.

• Delivery and Collaboration:
• Manage multiple assignments or projects simultaneously while meeting agreed quality, productivity, and turnaround expectations.
• Work effectively in a fast-paced environment, prioritize competing requests, and communicate risks or delays early.
• Partner with cross-functional and global teams, participate in reviews, and contribute to process and reporting improvements.
• Operate independently on defined tasks while seeking appropriate guidance for complex, ambiguous, or high-risk issues.
• Demonstrate professionalism, accountability, punctuality, attention to detail, and a goal-oriented approach.

Requirements

• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Engineering, Data Science, Commerce, or a related discipline.
• 3-4 years of experience in data analytics, business intelligence, data management, reporting, or a related technical role.
• Proven experience working with large datasets, performing data validation, resolving quality issues, and delivering accurate analytical outputs.
• Strong SQL skills, including joins, aggregations, common table expressions, window functions, and complex queries.
• Proficiency in Python for data manipulation and analysis, particularly pandas and NumPy.
• Good to have knowledge on Power BI, Tableau, or a comparable data-visualization platform.
• Good Microsoft Excel skills, including formulas, pivot tables, lookups, data analysis, and preferably Power Query.
• Working knowledge of statistics, trend analysis, model testing, and analytical problem-solving.
• Excellent written and verbal communication skills, including report writing, presentations, and stakeholder discussions.
• Strong organizational and time-management skills, with the ability to manage priorities and deliver work accurately within deadlines.
• Experience with web-based applications, internet research, Microsoft Office, and common data-management tools.