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Data Engineering Manager [Canada]

Dandy is transforming the massive ($200B) but antiquated dental industry. Backed by some of the world's leading venture capital investors, we are on an ambitious mission to integrate and simplify every function of the dental practice through technology. By building the operating system for every dental office in America, Dandy is empowering dentists with technology, innovation, and world-class support to achieve more for their practice, their people, and their patients.

Lead Dandy’s Centralized Data Engineering Team and Drive Innovation Across the Organization

At Dandy, we’re on a mission to revolutionize the dental industry, and we need a Data Engineering Manager to help build and lead our centralized data engineering function from the ground up. This is a unique and strategic leadership role where you will be responsible for shaping the foundation of Dandy's data infrastructure and creating the processes that will enable a high-performing, scalable, and collaborative data organization.

As the first leader of our centralized data engineering team, you will be at the forefront of building the systems, tools, and workflows that power the data-driven success of Dandy. You will work closely with data science, data analytics, and other cross-functional teams to ensure that data engineering provides the reliable, scalable infrastructure that enables all teams to accelerate decision-making and business impact.

If you're passionate about creating data systems that scale, optimizing workflows across teams, and building a best-in-class data engineering function from the ground up, we want to hear from you.

What You’ll Do

  • Build & Shape the Centralized Data Engineering Team: You will be the first leader of our centralized data engineering team, tasked with defining its structure, setting the team’s vision, and establishing the foundations of a high-performing data function. You’ll define roles, responsibilities, and career paths for data engineers and set the tone for the team’s growth and culture.

  • Create Scalable Data Engineering Processes: You’ll define and build the processes, methodologies, and tools that will shape the way we build, deploy, and maintain data pipelines across the organization. From setting standards for pipeline development and testing to implementing monitoring and data quality checks, you will create a framework that ensures high-quality, reliable data.

  • Drive Cross-Team Collaboration: Work closely with data science, data analytics, and business teams to understand their needs and deliver high-quality, actionable data. You’ll ensure that data engineering is the underlying layer that accelerates decision-making and business value for all teams. You will also serve as a bridge between technical teams and non-technical stakeholders, ensuring alignment and driving clarity on data initiatives.

  • Develop & Optimize the Data Platform: Lead the design and scaling of Dandy’s data platform, ensuring it supports cross-functional needs. You'll build a platform that integrates seamlessly with various data sources, tools, and systems, empowering teams to access, analyze, and utilize data faster and more effectively.

  • Increase Data Velocity & Throughput: Work to increase the velocity and throughput of data pipelines by optimizing processes and removing bottlenecks. By building robust, scalable infrastructure, you will enable teams to access and utilize data more quickly, helping to drive business decisions faster and with greater accuracy.

  • Implement Best Practices for Data Engineering: Establish and enforce best practices for data pipeline development, code quality, data documentation, testing, monitoring, and deployment. You'll ensure that all data systems are built with rigor and are scalable and maintainable in the long term.

  • Foster a Data-Driven Culture: Advocate for the use of data throughout the organization. You’ll help cultivate a culture where data is not just accessible, but trusted and actionable. Ensure that all teams have the data they need to drive insights, decision-making, and innovation.

What We’re Looking For

  • Leadership Experience: 3+ years of management experience in data engineering, with a proven track record of building and leading teams in a fast-paced, growth environment. You have experience shaping new functions and driving change within an organization.

  • Proven Technical Expertise: You have 6+ years of experience in data engineering, with hands-on expertise in designing and implementing data pipelines, ETL processes, and data platforms. You are skilled in Python, Airflow/Dagster, DBT, and cloud platforms like AWS or GCP.

  • Experience Building Centralized Teams: You’ve either built or scaled a centralized data engineering function, and you understand how to create the right team structure, processes, and culture to support growing business needs.

  • Strategic Thinking & Execution: You know how to define a long-term strategy for data infrastructure while also implementing the day-to-day tactical changes needed to make that strategy a reality. You are comfortable balancing strategic vision with hands-on execution.

  • Collaborative Leadership: You are a strong communicator and leader, capable of working with cross-functional teams, including data science, analytics, product, and business stakeholders, to ensure alignment and drive impactful data initiatives. You know how to break down complex technical issues into simple, actionable insights for non-technical teams.

  • Process-Oriented & Detail-Oriented: You’re experienced at designing and implementing processes that drive rigor, consistency, and efficiency across data workflows. You thrive in environments where systems are optimized for high performance and scalability.

  • Timezone Flexibility: You are based in a time zone within 3 hours of New York to ensure smooth collaboration with teams across the organization.

Bonus Points For

  • Experience Scaling Teams & Functions: You have experience hiring, onboarding, and developing data engineering talent in a growing organization. You know how to scale a function and build out the processes that support that growth.

  • Customer-Focused Approach: You understand how to deliver data solutions that serve both internal and external stakeholders, ensuring that the data infrastructure is aligned with customer and business needs.

  • Startup Experience: Experience working in early-stage or high-growth companies, where you had to be both strategic and hands-on in building the data infrastructure and enabling cross-team collaboration.

Job ID: J-392

For full time positions, we offer a wide range of best in class, comprehensive and inclusive employee benefits including healthcare, dental, parental planning, mental health benefits, a 401(k) plan, and paid time off.

Dandy is proud to be an equal-opportunity employer. We are committed to building a diverse and inclusive culture that celebrates authenticity to win as one. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, citizenship or immigration status, or any other legally protected characteristics.

Dandy also fully complies with the Americans with Disabilities Act (ADA). We are dedicated to embracing challenges and creating an accessible, inclusive workplace for all individuals. If you require any accommodations for your interview or have any questions beforehand, rest assured that we will move at Dandy speed and do everything in our power to meet your needs.

Average salary estimate

$135000 / YEARLY (est.)
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$120000K
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What You Should Know About Data Engineering Manager [Canada], Dandy

Dandy is on a mission to revolutionize the dental industry, and we’re looking for an enthusiastic Data Engineering Manager to be a pivotal part of this journey. As the first leader of our Centralized Data Engineering Team, you will have the incredible opportunity to shape the data infrastructure from scratch. Your passion for creating scalable data systems and optimizing workflows will be essential in this strategic role. You’ll collaborate closely with data science, analytics, and business teams, ensuring the delivery of dependable, high-quality data that enhances decision-making and drives business success. You’ll be responsible for defining the team structure and roles, while also establishing best practices for data pipeline development. Your leadership will coordinate the efforts to design and enhance Dandy’s data platform, allowing teams across the organization to utilize data effectively. Moreover, by focusing on increasing the velocity and throughput of data pipelines, you’ll help remove bottlenecks and ensure that insights come faster and more accurately. If you thrive on the challenge of building a best-in-class data engineering function and fostering a data-driven culture within an organization, then the role of Data Engineering Manager at Dandy is the perfect fit for you. Let’s work together to empower dental practices through innovative technology and exceptional support, making a meaningful impact across the industry.

Frequently Asked Questions (FAQs) for Data Engineering Manager [Canada] Role at Dandy
What are the responsibilities of a Data Engineering Manager at Dandy?

The Data Engineering Manager at Dandy has a variety of responsibilities, including leading the centralized data engineering team, shaping its structure and culture, and creating scalable data engineering processes. They will drive cross-team collaboration to ensure data quality and relevance, develop the data platform to integrate various sources, increase data velocity, and implement best practices in data engineering.

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What qualifications are required for the Data Engineering Manager position at Dandy?

Candidates for the Data Engineering Manager position at Dandy should have 3+ years of management experience in data engineering and over 6 years of hands-on experience in designing and implementing data pipelines and platforms. Proficiency in tools like Python, Airflow/Dagster, DBT, and familiarity with cloud platforms such as AWS or GCP is also required.

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How does Dandy foster collaboration across teams for its Data Engineering projects?

Dandy promotes collaboration in its data engineering initiatives by requiring the Data Engineering Manager to engage closely with cross-functional teams, including data science and analytics. This communication ensures alignment and clarity on data needs, allowing for efficient delivery of high-quality, actionable data to drive business impact across the organization.

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What kind of impact can a Data Engineering Manager have on Dandy's growth?

The Data Engineering Manager at Dandy plays a crucial role in accelerating the company's growth by creating a robust data infrastructure. This enables faster and more accurate decision-making, which is vital for driving business success. By instilling a culture of data trust and making data accessible and accountable, the manager can significantly increase the company’s operational efficiency and insights-driven strategies.

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What is the role of a Data Engineering Manager in shaping a data-driven culture at Dandy?

At Dandy, the Data Engineering Manager is instrumental in fostering a data-driven culture by advocating for data accessibility and usability across the organization. This involves not only implementing high standards for data quality and reliability but also encouraging teams to leverage data for insights, decision-making, and innovation, ensuring that data is integral to Dandy's operational mindset.

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Common Interview Questions for Data Engineering Manager [Canada]
Can you describe your experience building a data engineering team?

When addressing this question, highlight specific instances where you defined roles, shaped the team's vision, and implemented best practices to drive performance. Talk about the challenges you faced and how you overcame them, emphasizing your strategic and collaborative approach.

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How do you ensure data quality in your engineering processes?

Discuss the methods you've implemented, such as monitoring systems, testing protocols, and data validity checks. Providing concrete examples of successful implementations alongside how these practices improved data reliability will strengthen your response.

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What tools do you prefer for building data pipelines and why?

Mention specific tools like Python, Airflow/Dagster, and DBT that you’ve worked with, explaining your reasons for preferring them based on performance, community support, and your past experiences. Conveying your technical knowledge will show your proficiency in the role.

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How do you handle communication between technical teams and non-technical stakeholders?

Illustrate your approach to breaking down complex technical concepts into understandable terms for non-technical stakeholders. Providing examples where successful communication led to decision-making or project success will demonstrate your effectiveness in bridging this gap.

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What strategies do you use to enhance the velocity of data pipelines?

Cover techniques you've leveraged to optimize processes, improve efficiencies, or eliminate bottlenecks. Use real-world examples that showcase your results and ability to drive improvements within a data engineering context.

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How do you prioritize projects requests from various teams?

Describe your method for balancing competing priorities, perhaps using a relevant framework or process. Illustrating your capability to evaluate project impact and urgency will show your leadership in managing a data engineering team effectively.

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Can you share an experience where you successfully implemented a new data technology?

Provide a specific story detailing the implementation process, challenges faced, team involvement, and the resulting impact on data operations. Sharing measurable outcomes can further emphasize your success.

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What metrics do you track to assess the performance of your data engineering team?

Enumerate the key performance indicators (KPIs) and metrics you focus on, such as pipeline performance, data accuracy, and workflow efficiency. Discuss how these metrics guide your team's work and improve data strategy.

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How do you instill a culture of continuous improvement within your teams?

Discuss the practices you encourage, such as regular feedback loops, retrospectives, and knowledge sharing. Concrete examples where you've cultivated this culture will resonate with interviewers looking for growth-oriented leaders.

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What do you see as the biggest challenges in data engineering today?

Articulate your insights on current trends and challenges in the field, such as data governance, real-time processing, or cloud integration. Providing nuanced answers that reflect industry awareness will position you as a knowledgeable candidate.

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DATE POSTED
February 17, 2025

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