Technology Stack Analyst

WSP's Technology Stack Analyst position combines data validation, reporting automation, analytical problem-solving, and low-code application support in a professional consulting environment. Working with the US Strategic Growth Team, the selected candidate will help improve the reliability and usability of business data used for reporting and decision-making. This role is particularly relevant for graduates who enjoy working with datasets, identifying inconsistencies, building dashboards, and automating repetitive tasks. Candidates with academic projects or internship experience in Python, Excel, Power BI, or data analysis can demonstrate relevant skills even if they have limited full-time work experience.

What You Will Work On

The primary responsibility is to make business data easier to validate, understand, and use. Rather than focusing exclusively on dashboard creation, the role also involves identifying data problems and developing repeatable processes that help teams monitor and resolve them.

Key responsibilities include:

• Reporting and dashboard development: Build and maintain reports that highlight missing information, data gaps, duplicates, and inconsistencies across business systems.

• Data quality monitoring: Develop reporting workflows to assess data completeness, accuracy, and coverage, including information maintained in systems such as Horizon.

• Business reporting automation: Convert recurring reporting requirements into automated outputs, reducing repetitive manual work and improving consistency.

• Data cleansing support: Identify questionable records, document data issues, and help teams prioritize corrections through structured reports.

• Validation and quality assurance: Support validation rules and testing procedures to ensure that reports and workflows produce reliable results.

• Internal tool development: Assist with maintaining and improving applications and workflows developed using Microsoft Power Apps and Power Automate.

• Python-based analysis: Run existing Python processes or Jupyter notebooks, prepare datasets, perform basic transformations, and support reporting requirements.

• Feature testing: Check dashboards, automated workflows, and application updates before they are used by business teams.

The position requires attention to detail because inaccurate records or incomplete reporting can affect business decisions and the reliability of internal systems.

Technical Skills That Matter

WSP is looking for candidates who can work with data, understand reporting requirements, and learn the tools used by the team. The emphasis is on practical ability rather than advanced software engineering expertise.

Power BI, Excel, Python, and exposure to Microsoft Power Platform are particularly relevant to this position.

Candidates do not need to be experts in every listed technology. However, being able to explain how they have used at least some of these tools in a project, internship, or academic assignment can strengthen their application.

Data Quality in Practice

An important part of this role is recognizing that a report is only useful when the underlying data is trustworthy. The analyst will help identify data problems and create reporting logic that makes those problems visible to the teams responsible for resolving them.

For example, imagine a business system contains 10,000 customer records, but 700 records are missing a required business classification. An analyst could develop a report that identifies the affected records, calculates the percentage of incomplete data, and tracks the issue over time.

Practical checks relevant to this work include:

• Completeness: Determine whether required fields contain values.

• Uniqueness: Identify duplicate records using appropriate business identifiers.

• Consistency: Check whether related fields contain logically compatible information.

• Accuracy: Flag values that violate defined business rules or expected formats.

• Coverage: Measure whether required business entities or categories are represented in the dataset.

Candidates with SQL knowledge can also use queries to investigate data quality issues, even though SQL is not explicitly listed as a mandatory qualification in the supplied job description.

Understanding these concepts will help candidates explain their analytical approach during technical discussions.

How Automation Fits In

The role includes supporting repeatable reporting processes rather than relying entirely on manual spreadsheet work. Microsoft Power Automate can connect business applications and trigger actions when specified conditions are met, while Power Apps can provide interfaces through which employees interact with internal workflows.

Consider a reporting process in which a team receives an updated dataset every morning. A validation workflow could check for missing values, identify duplicate records, and prepare a report highlighting exceptions. The resulting output could help the responsible team investigate problems without manually reviewing every row.

The analyst may contribute by testing workflow conditions, checking report outputs, reviewing failures, and documenting expected behavior.

Python can complement these tools when data requires additional processing. For instance, a short script could standardize date formats, count null values across columns, or compare records between two datasets.

Candidates should understand the difference between automating a process, validating its output, and confirming that the business requirement has actually been met. All three are relevant to dependable reporting solutions.

Machine Learning Awareness

WSP also mentions foundational machine learning awareness as part of the qualifications. This is a baseline requirement, not an indication that the position is primarily a machine learning engineering role.

Candidates should be comfortable explaining basic concepts such as training datasets, model predictions, and evaluation metrics. They should also understand that model outputs can be affected by incomplete, inconsistent, or poorly prepared input data.

For example, if a model uses historical business records to classify incoming cases, missing values or inconsistent category labels may reduce the reliability of its predictions. A Technology Stack Analyst could help investigate data quality issues and support workflows that use those outputs.

Useful topics to review include supervised learning, classification, regression, training versus testing data, precision, recall, and the importance of representative datasets.

Who Can Apply

WSP's published qualifications specify a degree in Data Analytics, Computer Science, Information Systems, or a related discipline. The company is open to candidates with 0–3 years of experience, including recent graduates with relevant internship or project experience.

Candidates should also be prepared for employment background verification conducted through a third-party agency appointed by WSP India. Information and documents submitted during recruitment should be accurate and complete.

Work Environment and Collaboration

The position is based in Noida, Uttar Pradesh, and supports the US Strategic Growth Team. This creates an opportunity to understand how reporting and technology solutions support business teams operating across organizational and geographical boundaries.

WSP describes its working model as hybrid, designed to balance collaboration, productivity, and flexibility. However, the exact in-office attendance requirements and working hours for this particular position should be confirmed with the recruitment team.

The analyst is expected to understand business requirements, communicate data issues clearly, and coordinate with technology leadership and relevant business teams. The ability to explain a problem in straightforward language is valuable because stakeholders may not have the same technical background.

A useful communication example would be explaining that a report contains incomplete records, identifying which fields are affected, describing how the issue was detected, and outlining the validation needed before the report can be trusted.

How to Prepare for Screening

Candidates can prepare by revising the practical skills most closely connected to the advertised responsibilities.

Excel: Review lookup functions, conditional formulas, PivotTables, filtering, duplicate detection, and basic data-cleaning techniques.

Power BI: Understand data loading, Power Query transformations, relationships, basic DAX measures, and dashboard design. Be ready to describe a dashboard you have created and the decisions it supports.

Python: Practise reading CSV files with pandas, checking null values, detecting duplicates, standardizing data types, and generating simple summary reports.

Power Platform: Learn the distinction between Power Apps and Power Automate. Understand how a simple form-based application can connect to an automated approval or notification workflow.

Data validation: Prepare examples of completeness checks, uniqueness checks, business-rule validation, and exception reporting.

Machine learning: Review basic model terminology and why data quality matters when preparing inputs for analytical models.

A small portfolio project can provide useful evidence of these abilities. For example, a candidate could build a data quality dashboard that reports missing values, duplicate identifiers, invalid dates, and record counts, with a Python script preparing the validation results for visualization in Power BI.

When describing such a project, explain the original problem, the validation rules, the tools used, the issues discovered, and how the output helped a user understand the dataset.

How to Strengthen Your Application

Applicants should make their resumes specific to the responsibilities listed in this vacancy instead of relying on broad statements such as “good analytical skills” or “knowledge of programming.”

Include relevant projects that demonstrate data cleaning, dashboard development, report automation, or validation logic. State which tools were used and what the project accomplished. If a project involved Python and Power BI, describe how data was prepared, what the dashboard measured, and how the results were checked.

Candidates with limited exposure to Power Apps or Power Automate should distinguish between tools they have actually used and technologies they are currently learning. Accurate skill descriptions are more credible than claiming expertise without practical evidence.

The official vacancy does not specify a confirmed salary, selection-round structure, or exact work schedule. Candidates should verify these details through the recruitment process rather than relying on unofficial estimates.

Keywords for Resume

Technology Stack Analyst • Data Analytics • Data Quality • Data Validation • Data Cleansing • Microsoft Excel • Power BI • Python • Pandas • Jupyter Notebook • Microsoft Power Platform • Power Apps • Power Automate • Reporting Automation • Dashboard Development • Data Completeness • Data Accuracy • Data Integrity • Data Profiling • Anomaly Detection • Duplicate Detection • Business Intelligence • Data Transformation • Quality Assurance • Workflow Automation • Machine Learning Fundamentals • Analytical Problem-Solving • Stakeholder Collaboration

Final Career Perspective

The Technology Stack Analyst role at WSP is suited to candidates who want to combine data analysis with practical reporting and automation work. It offers an opportunity to build experience in data quality, business intelligence, Python-based validation, and Microsoft Power Platform solutions while supporting an international business team. Applicants who can demonstrate careful data handling, clear communication, and working knowledge of reporting tools will be better positioned to show how their skills match the role.