Data Analyst

Position summary

Renaissance Academy of Wisdom Private Limited is seeking a detail-oriented, analytical, and technically proficient Data Analyst SE to support data-driven decision-making across its education technology platforms, digital learning operations, recruitment systems, and business functions.

The role involves collecting, cleaning, validating, analysing, and interpreting data from multiple sources to produce accurate reports, interactive dashboards, performance metrics, and actionable business insights.

The successful candidate will work with management, technology, academic and training operations, recruitment, and other relevant teams to translate business requirements into measurable analytical outcomes. The candidate will also contribute to reporting automation, data quality improvement, performance monitoring, and the development of reliable business intelligence processes.

Position details

Job title

Data Analyst SE

Organisation

Renaissance Academy of Wisdom Private Limited

Department

Data Analytics, Business Intelligence and Operations

Reporting to

Analytics Manager, Business Operations Manager, or designated reporting manager

Employment type

Full-time, subject to approved employment terms

Location

As specified in the approved vacancy

Experience

Suggested range: 14 years, adjustable to the role's intended seniority

Education

Bachelor's or master's degree in a relevant discipline

Technical focus

SQL, Excel, Python, Power BI/Tableau, data quality and reporting

The title suffix “SE” is retained as provided. Its internal expansion, experience band, location, reporting line, and compensation should follow the company's approved HR structure.

3. Roles and responsibilities

A. Data collection and integration

The candidate will be responsible for collecting and consolidating data from relevant internal systems and authorised external sources.

Responsibilities include:

• Extracting information from databases, spreadsheets, learning management systems, recruitment platforms, assessment systems, and business applications.
• Understanding database structures, table relationships, fields, identifiers, and reporting requirements.
• Writing SQL queries to retrieve, filter, join, and aggregate information.
• Combining datasets from different systems while maintaining consistent identifiers and business definitions.
• Preparing analytical datasets for management reporting, operational analysis, and dashboard development.
• Documenting data sources, extraction logic, assumptions, and transformation steps.
• Coordinating with technical teams when data is unavailable, incomplete, inconsistent, or incorrectly structured.

B. Data cleaning, validation, and quality assurance

Data accuracy is essential because management decisions and operational reports depend on reliable information.

The candidate will:

• Identify missing values, duplicate records, inconsistent formats, and invalid entries.
• Standardise dates, categories, identifiers, names, and other fields as appropriate.
• Validate records against defined business rules and reference datasets.
• Reconcile data between source systems and final reports.
• Investigate differences in totals, record counts, status fields, and calculated metrics.
• Develop repeatable validation checks for recurring reports.
• Maintain records of identified issues, corrective actions, and outstanding discrepancies.
• Escalate source-system problems to the appropriate technical or operational team.

C. SQL and database analysis

The candidate should be comfortable working with relational databases and writing queries for business analysis.

Key responsibilities include:

• Writing SQL queries using SELECT, WHERE, JOIN, GROUP BY, HAVING, and aggregate functions.
• Using subqueries and common table expressions where appropriate.
• Analysing relationships across multiple tables.
• Creating summaries, breakdowns, and time-based comparisons.
• Investigating unusual results and identifying potential causes.
• Validating analytical calculations against source records.
• Improving query readability and efficiency.
• Supporting recurring extracts and reporting datasets.
• Maintaining appropriate access controls and following database security procedures.

D. Dashboard and business intelligence development

The Data Analyst SE will help convert raw information into understandable reports and interactive dashboards.

Responsibilities include:

• Developing dashboards using Power BI, Tableau, or similar tools.
• Creating KPI cards, trend charts, comparison views, filters, and drill-down reports.
• Defining clear metric calculations and maintaining consistent reporting standards.
• Designing dashboards for management, training, recruitment, technology, and operational users.
• Creating reports that highlight exceptions, performance gaps, and changes over time.
• Testing dashboards for calculation accuracy, usability, and data consistency.
• Updating reports as business requirements change.
• Documenting dashboard logic, refresh schedules, and data dependencies.

E. Python-based data analysis and automation

Python can be used to improve analytical efficiency and handle repetitive or complex data-processing tasks.

The candidate may be expected to:

• Use Python for data extraction, cleaning, transformation, and exploratory analysis.
• Work with Pandas and NumPy for tabular data manipulation and numerical calculations.
• Use Matplotlib or other suitable libraries for analytical visualisation.
• Automate recurring data preparation and reporting tasks.
• Develop scripts to validate datasets and identify anomalies.
• Compare outputs from automated processes against known results.
• Document scripts, assumptions, and dependencies.
• Collaborate with developers to integrate approved analytical workflows into existing systems.

Advanced machine learning is not necessarily a core requirement for this position unless specified by the project.

F. Education and learning analytics

Given the organisation's education and professional learning focus, the candidate may analyse learner and course performance data.

Responsibilities include:

• Measuring course enrolments, active learners, completion rates, and assessment outcomes.
• Analysing learner engagement and identifying patterns associated with course discontinuation.
• Monitoring performance across courses, cohorts, programmes, and training periods.
• Evaluating assessment participation, completion, scores, and pass rates.
• Identifying bottlenecks in learning journeys and course delivery.
• Preparing reports to help academic and training teams improve learning experiences.
• Supporting comparisons between course offerings and learning outcomes.
• Developing dashboards that allow authorised teams to monitor educational KPIs.

Any learner-level analysis should follow applicable privacy requirements and the organisation's data access policies.

G. Recruitment and HR analytics

Where recruitment or HR systems are within the assigned scope, the candidate may analyse operational and recruitment data.

Responsibilities include:

• Monitoring candidate registrations, applications, screening stages, interviews, and hiring outcomes.
• Measuring recruitment funnel conversion rates and time spent at each stage.
• Analysing interview scheduling, completion, and assessment status.
• Identifying delays and recurring bottlenecks in recruitment workflows.
• Preparing reports on hiring activity, candidate progress, and operational performance.
• Supporting HR reporting on attendance, leave, and other authorised workforce metrics where relevant.
• Reconciling information across recruitment and HR systems.
• Presenting trends that can support process improvements.

H. Business and operational analytics

The role may also support wider organisational reporting.

Responsibilities include:

• Preparing recurring business performance reports.
• Tracking operational KPIs and comparing actual results against targets.
• Analysing platform adoption, user activity, and workflow completion.
• Supporting management with trend analysis and business performance summaries.
• Identifying manual reporting activities that could be automated.
• Evaluating process changes using before-and-after performance comparisons.
• Investigating unexpected movements in business metrics.
• Producing evidence-based recommendations for operational improvements.

I. Requirements gathering and stakeholder management

• Meet with relevant teams to understand their analytical needs.
• Convert business questions into data requirements, calculations, and deliverables.
• Confirm KPI definitions and reporting expectations before development begins.
• Explain findings in clear language to non-technical stakeholders.
• Present assumptions, limitations, and relevant caveats alongside results.
• Manage feedback and implement approved changes to reports.
• Maintain documentation of requirements, deliverables, and revisions.
• Communicate risks and dependencies that may affect reporting timelines.

J. Data governance, privacy, and security

• Use authorised data sources and approved access methods.
• Protect confidential business, employee, candidate, and learner information.
• Follow the organisation's applicable data retention, privacy, and security policies.
• Limit access to sensitive data to authorised purposes.
• Avoid exposing personal information in unnecessary reports or dashboard views.
• Maintain appropriate audit trails and analytical documentation.
• Escalate suspected data quality or security issues through approved channels.

4. Technical skills and proficiency expectations

Technical area

Expected capability

SQL

Joins, aggregations, subqueries, CTEs, data validation

Microsoft Excel

PivotTables, lookup functions, conditional formulas, reconciliation

Power BI / Tableau

Interactive dashboards, KPI reporting, filters, visualisation

Python

Pandas, NumPy, data manipulation and automation

Statistics

Descriptive statistics, distributions, trend analysis, correlation basics

Database concepts

Relational models, keys, schemas, table relationships

Data quality

Deduplication, validation, reconciliation, anomaly identification

Reporting

Metric definitions, documentation, recurring report preparation

Communication

Business presentations, analytical summaries, stakeholder collaboration

Automation

Reusable scripts and repeatable reporting workflows

Core requirements: SQL, Excel, analytical reasoning, data quality, and reporting.

Additional advantages: Python automation, advanced Power BI, cloud data platforms, API-based data extraction, and experience with education or recruitment analytics.

5. Educational qualifications

Suitable qualifications may include:

• B.Tech or B.E. in Computer Science, Information Technology, or a related field.
• BCA or MCA.
• B.Sc. or M.Sc. in Statistics, Mathematics, Computer Science, Data Science, or Analytics.
• BBA or MBA with a strong analytical, quantitative, or business intelligence background.
• Other relevant qualifications supported by demonstrated technical proficiency.

Candidates should have a sound understanding of data interpretation, quantitative reasoning, structured problem-solving, and analytical reporting.

Relevant internships, practical projects, certifications, or professional experience may strengthen a candidate's profile.

6. Experience requirements

For candidates with 1–2 years of experience

Candidates should be able to:

• Write SQL queries for common analytical requirements.
• Clean and validate data using Excel or Python.
• Prepare recurring reports and basic dashboards.
• Understand KPI definitions and data relationships.
• Identify discrepancies and explain their findings.
• Work with guidance from senior team members.

For candidates with 3–4 years of experience

Candidates should additionally demonstrate the ability to:

• Independently manage analytical assignments.
• Design complex SQL queries and reusable reporting processes.
• Build and maintain dashboards for multiple stakeholders.
• Automate recurring reporting tasks.
• Investigate data inconsistencies and conduct root-cause analysis.
• Translate business requirements into measurable analytical solutions.
• Review data quality and reporting logic before delivery.
• Recommend process improvements based on analytical findings.

These experience bands are proposed guidelines and should be adjusted to the actual seniority intended by “SE.”

7. Key performance indicators

Performance should be assessed against measurable and role-appropriate objectives.

KPI

What it measures

Report accuracy

Reliability of analytical outputs and calculations

On-time delivery

Completion of assigned reports within agreed deadlines

Data quality

Detection and resolution of data inconsistencies

Dashboard reliability

Consistency and usability of dashboards

Automation efficiency

Reduction in avoidable manual reporting effort

Issue resolution

Effectiveness in investigating discrepancies

Stakeholder satisfaction

Usefulness and clarity of reports and analysis

Documentation quality

Completeness of query, metric, and process documentation

Process improvement

Measurable improvements resulting from analytical work

Targets should be agreed with the reporting manager based on the assigned responsibilities and available data.

8. Illustrative work scenarios

Scenario 1 — Learning platform analytics

Business question: Why are some courses experiencing low completion rates?

Expected approach: Extract enrolment and progress data, validate completion records, compare course and cohort performance, identify where learners stop progressing, and prepare a dashboard for the learning team.

Expected output: A validated completion report, engagement dashboard, and evidence-based observations for further investigation.

Scenario 2 — Recruitment workflow analytics

Business question: Which recruitment stages are creating delays?

Expected approach: Analyse candidate stage histories, interview dates, status changes, and turnaround times. Validate that completed and active records are classified correctly.

Expected output: A recruitment funnel dashboard showing stage-wise conversion, elapsed time, and areas requiring operational review.

Scenario 3 — Reporting automation

Business question: How can repetitive management reports be produced more efficiently?

Expected approach: Document the existing workflow, identify repeatable calculations, develop a suitable SQL or Python process, and validate automated results against the current report.

Expected output: A documented, repeatable reporting process with appropriate validation and reduced manual effort.

These are illustrative examples of work aligned with the organisation's business context, not statements that every system or project will form part of the role.

9. Soft skills and professional competencies

The ideal candidate should demonstrate:

• Strong analytical and logical reasoning.
• Attention to detail and commitment to accuracy.
• Structured problem-solving and investigative thinking.
• Clear written and verbal communication.
• Ability to explain technical results in business terms.
• Effective time management and task prioritisation.
• Accountability for deliverables and reporting quality.
• Collaboration across technical and non-technical teams.
• Adaptability to changing business requirements.
• Professional conduct and respect for confidentiality.

10. Suggested selection process

The following is a recommended recruitment framework for this role.

1. Resume screening
   Review educational qualifications, relevant experience, SQL, Excel, dashboard development, and analytical projects.
2. Technical assessment
   Test SQL queries, data cleaning, Excel formulas, data interpretation, and basic statistics.
3. Practical analytics exercise
   Ask candidates to analyse a sample dataset and prepare a concise report or dashboard.
4. Technical interview
   Assess analytical reasoning, query logic, validation methods, Python skills, and problem-solving.
5. Managerial or business round
   Evaluate communication, stakeholder handling, prioritisation, and understanding of business metrics.
6. HR discussion
   Confirm role expectations, availability, compensation, location, and applicable employment checks.

11. Career development opportunities

Subject to performance, business needs, and available opportunities, this role can provide experience relevant to future positions such as:

• Senior Data Analyst
• Business Intelligence Analyst
• Power BI Developer
• Business Analyst
• Analytics Consultant
• Data Analytics Lead
• Data Engineer, with additional engineering expertise

The role can also develop expertise in learning analytics, SaaS product analytics, recruitment analytics, and operational intelligence.