The Senior Data Science Engineer will work within the Development organization to turn data into insights and solutions that guide product decisions, improve software development processes, and create business value. The role involves leading projects across the full data science lifecycle, from understanding business needs and building data pipelines to deploying analytics solutions and helping teams make informed decisions. Responsibilities include: Leading initiatives that increase adoption of data-informed decision-making; Partnering with Subject Matter Experts (SMEs) to define success metrics and evaluate impact; Designing, building, and maintaining reliable data pipelines for reporting, analytics, and modeling using AWS and Snowflake; Analyzing quantitative and qualitative data using statistical, machine learning, and LLM-based techniques; Developing and maintaining dashboards and reports using Power BI and/or MATLAB; Evaluating and implementing AI-assisted and agent-based systems to enhance workflows. Qualifications: A bachelor's degree and 6 years of professional work experience, or a master's degree and 3 years of professional work experience, or a PhD degree is required. Additional requirements include strong data engineering skills, knowledge of ETL processes, experience building production data pipelines, strong understanding of metrics design, statistics, probability, and proficiency in SQL, Python, and/or MATLAB. Strong partnership, facilitation, and communication skills are essential. MathWorks operates a hybrid work model.
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Skills for this role
Data EngineeringETLAWSSnowflakeStatisticsMachine LearningLLMSQLPythonMATLABPower BITableauData VisualizationAI-assisted systemsAgent-based systems
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A focused preparation guide, built around this job’s responsibilities and requirements.
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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
End-to-End Data Lifecycle Management
The role requires leading projects from initial business requirement gathering through to the deployment of analytics solutions. You must demonstrate the ability to bridge the gap between technical data engineering tasks—such as building pipelines in AWS and Snowflake—and the practical application of these insights to guide product and process decisions.
Stakeholder Influence and Metric Definition
A core priority is partnering with Subject Matter Experts to define success metrics. You must be able to facilitate discussions with non-technical stakeholders to ensure that the data models and dashboards you build directly translate into actionable business value and improved software development processes.
Advanced Analytical Implementation
Beyond standard reporting, you are expected to apply statistical, machine learning, and LLM-based techniques to solve complex problems. This includes evaluating and implementing AI-assisted or agent-based systems to optimize internal workflows, requiring a balance of research-oriented thinking and production-grade engineering.
Likely challenges to think through
Balancing Technical Depth with Cross-Functional Facilitation
The challenge lies in maintaining high-level data engineering standards while simultaneously acting as a change agent within the Development organization. Prepare by practicing how you explain complex technical trade-offs in data pipeline architecture to stakeholders who are primarily focused on the business impact of the resulting metrics.
Adapting to Diverse Tooling Environments
The role requires proficiency in a mix of industry-standard tools (Python, SQL, AWS, Snowflake) and MathWorks-specific environments (MATLAB). You may face difficulty if you rely solely on one ecosystem; prepare by highlighting your ability to integrate disparate data sources and your flexibility in choosing the right tool—whether Power BI or MATLAB—for specific stakeholder needs.
03
Practice your answers
Technical practice
Designing Production Data Pipelines on AWS and Snowflake
What to coverExplain your approach to building reliable, scalable ETL pipelines in a cloud environment. Discuss how you handle data quality, schema evolution, and monitoring to ensure data availability for downstream analytics.
Metrics Design and Statistical Validation
What to coverDescribe your process for partnering with SMEs to define success metrics for a new initiative. How do you ensure these metrics are statistically sound and effectively measure the intended business impact?
Applying LLM-based Techniques in Data Science
What to coverDiscuss a scenario where you have applied LLM-based techniques to analyze qualitative or quantitative data. Address the challenges of integrating these models into existing workflows and how you validate their outputs.
Machine Learning Model Lifecycle Management
What to coverDetail your experience in taking a machine learning model from initial analysis to production deployment. Focus on how you manage model performance, retraining, and integration with existing software development processes.
Data Visualization and Stakeholder Communication
What to coverWhen building dashboards in Power BI or MATLAB, how do you translate complex technical insights into actionable information for non-technical stakeholders? Provide an example of how a visualization influenced a specific decision.
Implementing AI-Assisted and Agent-Based Systems
What to coverHow do you evaluate the feasibility and impact of implementing agent-based systems to enhance data science workflows? Discuss the trade-offs between automation and human oversight in these systems.
SQL and Python Optimization for Large Datasets
What to coverDescribe techniques you use to optimize SQL queries or Python code when working with large-scale datasets in Snowflake. Focus on performance tuning and resource management.
Statistical Rigor in Decision-Making
What to coverHow do you apply probability and statistical methods to validate findings when data is noisy or incomplete? Explain how you communicate the level of confidence in your results to leadership.
Behavioural practice
Influencing Data-Informed Decision Making
What to coverDescribe a situation where you had to convince stakeholders to adopt a data-driven approach instead of relying on intuition. What was the situation, what actions did you take to influence them, and what was the result?
Partnering with Subject Matter Experts
What to coverGive an example of a project where you collaborated with SMEs who had limited data science experience. How did you facilitate the partnership to ensure the project met both technical and business requirements?
Leading Projects Across the Data Lifecycle
What to coverTell me about a time you led a data science project from conception to deployment. How did you manage the different phases, handle unexpected roadblocks, and ensure the final solution delivered business value?
Navigating Ambiguity in Business Needs
What to coverDescribe a time when the business requirements for a data project were unclear or evolving. How did you work to clarify these needs and align your technical strategy with the shifting goals?
02
Sharpen your knowledge
Topics to prepare
Metrics Design and Stakeholder Alignment
The role requires partnering with SMEs to define success metrics. Exercise: Practice translating high-level business goals into quantifiable KPIs. Create a mock scenario where you must define success metrics for a software development process improvement, then draft a communication plan to present these metrics to non-technical stakeholders.
Data Science Lifecycle Management
You will lead projects from business need identification to deployment. Exercise: Map out a full data science project lifecycle for a hypothetical product feature. Identify potential bottlenecks in data pipeline reliability and explain how you would mitigate them using AWS and Snowflake infrastructure.
AI-Assisted and Agent-Based Systems
The role involves evaluating and implementing AI-assisted workflows. Exercise: Research current agent-based frameworks and evaluate how they could automate a specific, repetitive data science task, such as automated data cleaning or model monitoring, to improve team efficiency.
Skills to revise
Production Data Pipelines (AWS/Snowflake)
You must build and maintain reliable pipelines. Exercise: Design a robust ETL workflow using SQL and Python that extracts data from a source, transforms it for analytical use, and loads it into a Snowflake environment, ensuring error handling and logging are included.
Statistical Analysis and Machine Learning
The role requires analyzing quantitative data using statistical and ML techniques. Exercise: Select a dataset and perform a rigorous statistical analysis to validate a hypothesis. Document your methodology, including how you chose your metrics and how you addressed potential biases in the data.
Data Visualization and Reporting
You will use Power BI or MATLAB to communicate insights. Exercise: Create a dashboard that visualizes a complex dataset, focusing on clarity and actionable insights. Ensure the dashboard effectively communicates the 'why' behind the data to a stakeholder audience.
04
Your preparation checklist
Prepare a project walkthrough on data pipeline architecture
Develop a concise presentation detailing a production-grade data pipeline you built using AWS and Snowflake. Focus on your approach to ETL processes, data reliability, and how you ensured the pipeline supported downstream analytics or modeling.
Review statistical and machine learning fundamentals
Refresh your knowledge of probability and statistics, specifically regarding metrics design and evaluation. Be prepared to discuss how you select and validate machine learning models or LLM-based techniques to solve specific business problems.
Draft examples of stakeholder influence and partnership
Prepare specific anecdotes demonstrating how you partnered with Subject Matter Experts to define success metrics. Highlight instances where you used data visualization (Power BI or MATLAB) to influence product or process decisions.
Research AI-assisted and agent-based system workflows
Formulate a perspective on how AI-assisted or agent-based systems can optimize data science workflows. Be ready to discuss the trade-offs and implementation challenges of integrating these technologies into existing development processes.
Formulate strategic questions for the interviewers
Prepare questions regarding the current data maturity of the Development organization, how the team balances long-term data engineering projects with ad-hoc analytical requests, and how the team measures the impact of their data-informed initiatives.
Availability last checked: 19 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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