A Data Analyst at TELUS Digital plays a critical role in shaping the future of digital customer experience (DLCX) and AI-driven solutions. As a global leader in digital enablement, TELUS Digital processes massive volumes of interaction, operational, and performance data daily. In this role, you do not just generate reports; you act as a strategic advisor, extracting actionable insights that directly influence service delivery, product enhancement, and operational efficiency for some of the world's most prominent brands.

The impact of this position is felt across multiple business units. Whether you are optimizing data pipelines, identifying anomalies in customer engagement metrics, or building sophisticated dashboarding suites, your work directly informs executive-level decision-making. You will collaborate closely with cross-functional teams, including product managers, software engineers, and operations leaders, to translate complex data structures into clear business strategies.

What makes this role uniquely compelling is the sheer scale and diversity of the data you will handle. From structured sales metrics to unstructured conversational data, a Data Analyst at TELUS Digital must navigate complex, sometimes messy datasets with high precision. To succeed, you must combine robust technical skills with a sharp product sense, transforming raw numbers into narratives that drive business growth.

The questions you will encounter during the TELUS Digital interview process are designed to evaluate your technical execution, problem-solving structure, and communication clarity. These questions are compiled from real interview experiences and are categorized to help you identify patterns and focus your preparation.

SQL & Data Manipulation

This category tests your ability to query, filter, and structure data efficiently. Expect a strong focus on relational database concepts and performance optimization.

• Write a query to find the top three sales regions by monthly revenue growth, utilizing Common Table Expressions (CTEs).
• How do you handle NULL values in a GROUP BY clause, and what are the performance implications of using LEFT JOIN versus INNER JOIN on large datasets?

To stand out in the TELUS Digital hiring process, you must demonstrate a balanced combination of technical mastery and business acumen. Your preparation should focus on the core areas that interviewers evaluate most closely.

Role-Related Knowledge – You must demonstrate a deep understanding of data structures, SQL optimization, and visualization best practices. Be prepared to explain not just how you write a query or build a dashboard, but why you chose that specific approach over alternatives.

Problem-Solving & Messy Data Management – In the real world, data is rarely clean. Show your interviewers that you are comfortable navigating poorly defined columns, missing values, and ambiguous business requirements. Your ability to systematically clean and structure data is just as important as your final analysis.

Executive Presentation & Communication – A great analysis is useless if stakeholders cannot understand it. You will be evaluated on your ability to synthesize complex technical findings into clear, high-impact business recommendations. Focus on structured frameworks, concise slides, and actionable takeaways.

Adaptability & Drive – TELUS Digital operates in a fast-paced environment. Show that you are proactive, comfortable with rapid changes in project scope, and eager to learn new tools and domains as business needs evolve.

The interview process for a Data Analyst at TELUS Digital typically follows one of two distinct pathways, depending on whether the role is a specialized corporate position or a high-volume analytical/rater track. Understanding which track you are on is essential for targeting your preparation.

For high-volume or localized analytical positions, the process is highly automated and centers around a rigorous qualification exam. Candidates are provided with comprehensive study materials and given a set timeframe (usually one week) to review the guidelines and complete an online test consisting of approximately 45 questions. Passing this test is often the primary or sole requirement for securing an offer.

For corporate and specialized Data Analyst roles (such as Analyst, Data Management), the process is multi-staged and highly interactive. It begins with a talent acquisition screening call to review your background and salary expectations. This is followed by a technical assessment, which frequently takes the form of a take-home business case study involving a messy dataset. You will be required to clean the data, perform a comprehensive analysis, build a dashboard (typically in Power BI), and present your insights and strategic recommendations to a panel of stakeholders. The final stage consists of a deep-dive behavioral interview with the hiring manager and team members to assess culture fit and collaboration style.

The visual timeline above outlines the typical stages of the corporate hiring track. Candidates should expect a rigorous evaluation of both technical skills and presentation capabilities, with the case study presentation acting as the pivotal decision point. Use this timeline to pace your preparation, ensuring you allocate sufficient time to practice live data presentation and SQL exercises.

To excel at TELUS Digital, you must understand the specific technical and analytical dimensions on which you will be evaluated.

SQL & Relational Databases

SQL is the foundational technical requirement for any analytical role at TELUS Digital. Interviewers want to see that you can write clean, efficient, and well-structured queries under pressure.

Be ready to go over:

• Complex Joins and Set Operations – Understanding when to use outer, inner, or self-joins to combine disparate data sources.
• Aggregation and Grouping – Utilizing GROUP BY, HAVING, and nested aggregation functions to summarize operational metrics.
• Subqueries and CTEs – Structuring complex logic using Common Table Expressions to keep your queries readable and maintainable.
• Advanced concepts (less common) – Window functions (e.g., ROW_NUMBER(), LAG(), LEAD()), query performance tuning, and indexing strategies.

Example questions or scenarios:

• "Write a SQL query to identify customers who made a purchase in consecutive months using window functions."
• "How would you optimize a query that is running slowly due to multiple nested subqueries?"

Case Study, Visualization & Presentation

The case study is designed to simulate a real-world project at TELUS Digital. You will be handed a dataset and a business problem, and you must deliver both the technical solution and the executive narrative.

Be ready to go over:

• Data Cleaning and Preparation – Handling missing data, correcting inconsistent data types, and defining ambiguous columns.
• Dashboard Design (Power BI/Tableau) – Creating intuitive, visually clean, and interactive reports that highlight key performance indicators (KPIs).
• Anomaly Detection – Identifying unexpected spikes, drops, or patterns in the data and formulating hypotheses to explain them.
• Advanced concepts (less common) – Designing automated data refresh schedules, implementing row-level security in dashboards, and defining custom DAX measures.

Example questions or scenarios:

• "Present your findings on this sales dataset to a mock executive panel, highlighting three critical business risks and your proposed solutions."
• "Why did you choose this specific visualization type to represent customer churn over time?"

Programming & Automation (Python, Excel, VBA)

Depending on the specific team, you may be expected to automate repetitive data workflows or perform advanced statistical analysis.

Be ready to go over:

• Advanced Excel Formulas – Utilizing nested logical functions, array formulas, and dynamic array functions to manipulate data.
• VBA Scripting – Writing macros to automate data extraction, formatting, and reporting pipelines.
• Python Data Stack – Using Pandas, NumPy, and Matplotlib/Seaborn for data manipulation and exploratory data analysis.
• Advanced concepts (less common) – Writing Python scripts to interact with APIs, automating SQL database updates via Python, and basic machine learning concepts for predictive modeling.

Example questions or scenarios:

• "Walk through a Python script you wrote to clean a messy CSV file and export the summarized results to an Excel sheet."
• "Write a VBA macro that loops through all sheets in a workbook and consolidates rows matching a specific condition."

As a Data Analyst at TELUS Digital, your day-to-day work will be highly dynamic, bridging the gap between technical data engineering and strategic business operations.

Your primary responsibility will be the ownership of data pipelines and reporting suites. You will design, build, and maintain robust data models and dashboards that track key operational, financial, and customer experience metrics. This involves writing complex SQL queries to extract data from warehouse environments, cleaning and transforming that data, and feeding it into visualization tools like Power BI.

Collaboration is a core component of this role. You will work closely with product managers to define tracking requirements for new features, assist engineering teams in ensuring data integrity, and partner with operations leaders to identify bottlenecks in service delivery. You will regularly present your analytical findings to senior leadership, transforming complex technical insights into clear, actionable business recommendations.

Additionally, you will play a key role in data governance and management. This includes defining data standards, documenting data dictionaries, and ensuring that data collection processes comply with privacy and security regulations. You will also be proactive in identifying opportunities to automate manual reporting processes, using tools like Python, VBA, or SQL scheduled jobs to drive team efficiency.

To be competitive for a Data Analyst position at TELUS Digital, you should possess a strong blend of technical expertise, analytical curiosity, and communication skills.

• Must-have technical skills – Strong proficiency in SQL (writing complex queries, CTEs, and joins), experience building interactive dashboards in Power BI or Tableau, and advanced Excel skills (complex formulas, pivot tables).
• Nice-to-have technical skills – Experience with Python (Pandas, NumPy) for data manipulation, familiarity with VBA for Excel automation, and knowledge of cloud data warehouses (such as Snowflake, Google BigQuery, or AWS).
• Experience level – Typically 2–4 years of experience in a data analytics, business intelligence, or data management role. A degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or Business Analytics is highly preferred.
• Soft skills – Exceptional communication and presentation skills, the ability to work independently under tight deadlines, a strong attention to detail, and a proactive problem-solving mindset.