Senior Business Analytics Consultant

As a Senior Business Analytics Consultant at ShyftLabs, your responsibilities include designing high-quality technical solutions, developing and testing data models to capture client KPIs, and working closely with clients to develop Cloud-based solutions. You will also analyze full-funnel marketing data to discover insights that drive value, contribute to our product vision, and solve technical issues as they arise. It's a role that blends both technical expertise and strategic business acumen.

To qualify for the Senior Business Analytics Consultant role at ShyftLabs, you should have at least 5 years of experience in data analysis and a minimum of 2 years in retail or e-commerce. Strong working experience with SQL and Python is essential, along with CRM and Marketing Analytics knowledge. Familiarity with Cloud Data warehouses like Snowflake or BigQuery is also required. A Bachelor’s or Master’s Degree in a relevant field is necessary as well as client-facing experience with executive-level stakeholders.

ShyftLabs stands out as a unique workplace for a Senior Business Analytics Consultant due to our commitment to innovation and employee growth. We work exclusively with Fortune 500 companies, meaning your efforts have a significant impact. Our culture emphasizes teamwork, continuous learning, and development, as well as inclusivity and diversity. Additionally, we offer a flexible work environment that allows our team members to work fully remotely from anywhere in Canada.

At ShyftLabs, we prioritize the professional development of our employees by offering extensive learning resources and opportunities for growth. As a Senior Business Analytics Consultant, you'll have access to tools, training, and workshops aimed at enhancing your skills and keeping you current with industry trends. We believe that when our employees grow, so does our company, and we are committed to providing the support you need to succeed.

The company culture at ShyftLabs is centered around collaboration, innovation, and inclusivity. We foster an environment where all perspectives are valued, and team members are encouraged to share their ideas. As a Senior Business Analytics Consultant, you'll be part of a supportive team that prioritizes quality solutions and embraces new challenges, making every day an opportunity to learn and grow together.

When preparing your answer, discuss specific SQL projects you've completed, emphasizing how you used it to analyze data, develop reports, or derive insights that informed business decisions. Highlight instances where your SQL skills led to improvements in data quality or efficiency.

Provide a step-by-step overview of your approach to developing data models, including gathering requirements, understanding client business objectives, and testing the model iteratively to ensure it aligns with the KPIs. Use examples from past experiences to illustrate your process.

Choose a project where you collaborated with cross-functional teams, highlighting how you communicated complex data insights to non-technical stakeholders. Discuss the challenges you faced, how you resolved them, and the impact of your work on the project's success.

Discuss your preferred data visualization tools, such as Looker or Tableau, and explain why you find them effective. Be sure to mention specific features that have helped you convey insights clearly and engagingly to clients and stakeholders.

When answering, detail your hands-on experience with building and deploying Machine Learning models. Discuss the types of models you’ve created, the datasets you’ve worked with, and the outcomes that resulted from leveraging these models to enhance decision-making.

Provide an example of a significant technical challenge you've encountered, outlining your thought process in troubleshooting and resolving the issue. Highlight your problem-solving skills and your willingness to seek help or collaborate with others when necessary.

Share your knowledge of Cloud Data warehouses such as Snowflake or BigQuery, detailing specific projects where you've utilized these technologies. Emphasize your understanding of scalability, performance, and data integration within a Cloud environment.

Discuss your approach to data quality management, including methods for identifying data inconsistencies, implementing data cleansing processes, and monitoring data quality over time. Share examples of how you've successfully improved data quality in past projects.

Explain your methods for staying informed about industry trends, such as attending conferences, participating in webinars, or following influential thought leaders on social media. Mention any relevant publications or resources you regularly review to remain at the forefront of data analytics.

Provide your strategy for task prioritization, discussing how you assess deadlines, project requirements, and potential impacts on clients. Share examples of how effective prioritization has led to successful project outcomes in your previous roles.