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Data Engineer

We believe small businesses are at the heart of our communities, and championing them is worth fighting for. We empower small business owners to manage their finances fearlessly, by offering the simplest, all-in-one financial management solution they can't live without.


Reporting to the Senior Manager of AI & Data Platform, as a Data Engineer you will be building tools and infrastructure to support efforts of the Data Products and Insights & Innovation teams, and the business as a whole.


We’re looking for a talented, curious self-starter who is driven to solve complex problems and can juggle multiple domains and stakeholders. This highly technical individual will collaborate with all levels of the Data and AI team as well as the various engineering teams to develop data solutions, scale our data infrastructure and advance Wave to the next stage in our transformation as a data-centric organization.


This role is for someone with proven experience in complicated product environments. Strong communication skills are a must to bridge the gap between technical and non-technical audiences across a spectrum of data maturity.


Here’s How You Make an Impact:
  • You’re a builder. You’ll be responsible for designing, building and deploying the components of a modern data stack, including CDC ingestion (using Debezium), a centralized Hudi data lake, and a variety of batch, incremental and stream-based pipelines.
  • You’ll make things better. You enjoy the challenge of helping build and manage a fault tolerant data platform that scales economically, while balancing innovation with operational stability by maintaining legacy Python ELT scripts and accelerating the transition to dbt models in Redshift.
  • You’re all about collaboration and relationships. You will collaborate within a cross-functional team in planning and rolling out data infrastructure and processing pipelines that serve workloads across analytics, machine learning and GenAI services. You enjoy working with different teams across Wave and helping them to succeed by ensuring that their data, analytics, and AI insights are reliably delivered.
  • You’re self-motivated and can work autonomously. We count on you to thrive in ambiguous conditions by independently identifying opportunities to optimize pipelines and improve data workflows under tight deadlines.
  • You will resolve and mitigate incidents: You will respond to PagerDuty alerts and proactively implement monitoring solutions to minimize future incidents, ensuring high availability and reliability of data systems.
  • You're a strong communicator. As a data practitioner, you’ll have people coming to you for technical assistance, and your outstanding ability to listen and communicate with people will reassure them as you help answer their concern.
  • You love helping customers. You will assess existing systems, optimize data accessibility, and provide innovative solutions to help internal teams surface actionable insights that enhance external customer satisfaction.


You Thrive Here By Possessing the Following:
  • Data Engineering Expertise: Bring 3+ years of experience in building data pipelines and managing a secure, modern data stack. This includes CDC streaming ingestion using tools like Debezium into a Hudi data lake that supports AI/ML workloads and a curated Redshift data warehouse.
  • AWS Cloud Proficiency: At least 3 years of experience working with AWS cloud infrastructure, including Kafka (MSK), Spark / AWS Glue, and infrastructure as code (IaC) using Terraform.
  • Strong Coding Skills: Write and review high-quality, maintainable code that enhances the reliability and scalability of our data platform. We use Python, SQL, and dbt extensively, and you should be comfortable leveraging third-party frameworks to accelerate development.
  • Data Lake Development: Prior experience building data lakes on S3 using Apache Hudi with Parquet, Avro, JSON, and CSV file formats.
  • Workflow Automation: Build and manage multi-stage workflows using serverless Lambdas and AWS Step Functions to automate and orchestrate data processing pipelines.
  • Data Governance Knowledge: Familiarity with data governance practices, including data quality, lineage, and privacy, as well as experience using cataloging tools to enhance discoverability and compliance.
  • CI/CD Best Practices: Experience developing and deploying data pipeline solutions using CI/CD best practices to ensure reliability and scalability.
  • Data Integration Tools: Working knowledge of tools such as Stitch and Segment CDP for integrating diverse data sources into a cohesive ecosystem.
  • Analytical and ML Tools Expertise: Knowledge and practical experience with Athena, Redshift, or Sagemaker Feature Store to support analytical and machine learning workflows is a definite bonus!


Succeeding at Wave:  At Wave, you’ll have the chance to grow and thrive by building scalable data infrastructure, enhancing a modern data stack, and contributing to high-impact projects that empower insights and innovation across the company. Whether collaborating in our vibrant downtown Toronto hub or working remotely, you’ll have the flexibility to shape your journey and make a lasting impact on Wave’s data-driven future. At Wave, we value diverse perspectives and encourage open, respectful feedback, fostering an inclusive environment where innovation flourishes, and every team member has the opportunity to grow.


At Wave, you’re treated like the incredible human being you are. 


Work From Where You Work Best: We will always have a welcoming, energizing, and world-class office (in Toronto) with a space for you. Or, if you’re more comfortable working from home, the choice is yours.

We Care About Future You: You will stretch yourself and you will grow at Wave. You will also be supported on this journey with diverse learning experiences, educational allowances, mentorship, and so much more.

We Support the Full You: We make a serious investment in your health & wellness. When we think about benefits we think about body, mind, & soul and we take this stuff very seriously. 

We Take Care of the Fundamentals: Fair compensation, all the office perks you’d want, and the various goodies you’d expect from a growing tech company. This is the obvious stuff, but we don’t want you to think we forgot!


We believe that a diverse and inclusive culture creates the best workplace. We embrace our differences, value individuality, and the broad spectrum of every Waver's skills and abilities. We challenge each other from a place of respect and pursuit of continuous growth. We trust each other and encourage everyone to bring their authentic selves to work, everyday. As Wavers, our voices matter, our opinions are met with an open mind. The best ideas win, no matter whose they are.  Contributing to an inclusive culture is a part of all of our job descriptions. 


We’ve been continuously recognized as one of Canada's Top Ten Most Admired Corporate Cultures and one of Canada’s Great Places to Work in categories including Technology, Millennials, Mental Health, Inclusion and Women.  


Are you ready to be a Waver? Join us!

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What You Should Know About Data Engineer, Wave HQ

At Wave, we truly believe that small businesses are the heartbeat of our communities, and we're dedicated to empowering them by providing the most intuitive financial management solutions. We're currently on the lookout for a talented Data Engineer to join our vibrant team in Toronto, Ontario. As a Data Engineer at Wave, you'll be at the forefront of our mission, working closely with the Senior Manager of AI & Data Platform to build robust tools and infrastructure that will bolster our Data Products and Insights & Innovation teams. If you thrive on solving complex challenges and enjoy collaborating across various domains, you’ll fit right in! You’ll design and deploy key components of our modern data stack and ensure operational stability while managing our data platform. Strong coding skills in Python, SQL, and familiarities with AWS functionalities like Kafka and Glue will serve you well in this role. Plus, your ability to communicate effectively will help bridge the gap between technical jargon and the needs of stakeholders from all backgrounds. If you're someone who loves the challenge of optimizing workflows and enhancing data accessibility for our internal teams, we would love to see you contribute to delivering actionable insights that can truly elevate customer satisfaction. The possibilities are endless, and we are excited about the impact you'll make here at Wave!

Frequently Asked Questions (FAQs) for Data Engineer Role at Wave HQ
What makes the Data Engineer position at Wave in Toronto unique?

The Data Engineer position at Wave offers a unique blend of creative problem-solving and technical prowess, focusing on building a robust data infrastructure that supports small businesses. As part of a cross-functional team in Toronto, you'll be instrumental in driving the company toward becoming a more data-centric organization. Collaborating closely with teams responsible for analytics, machine learning, and customer insights ensures that your work directly contributes to tangible outcomes for small business owners.

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What qualifications are necessary for a Data Engineer role at Wave?

To be successful as a Data Engineer at Wave, candidates should have at least 3 years of experience in building secure data pipelines, knowledge of AWS cloud infrastructure, and proficiency in coding with Python and SQL. Familiarity with tools like Debezium for CDC ingestion and Apache Hudi for data lake management will prove invaluable. Strong communication skills to liaise with both technical and non-technical audiences are equally important for this role.

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What is the work environment like for a Data Engineer at Wave?

Wave promotes a supportive and inclusive work environment, allowing for flexibility between working in our vibrant downtown Toronto office and working remotely. As a Data Engineer, you will be a key player in a culture that values diversity, encourages collaboration, and provides opportunities for professional growth. With focused initiatives on health, wellness, and continuous learning, you will feel energized to contribute your best work.

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What tools and technologies will a Data Engineer at Wave use?

In the Data Engineer role at Wave, you will engage with a modern data stack that includes AWS services like Kafka, DynamoDB, and Spark, as well as tools such as dbt for data modeling and Terraform for infrastructure as code. Your day-to-day will involve designing data pipelines using tools like Stitch or Segment, building data lakes on S3 with Hudi, and managing various data formats to ensure smooth data flow and accessibility across the organization.

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How does Wave support the career development of its Data Engineers?

At Wave, we are committed to the continuous learning and growth of our employees, especially our Data Engineers. You will have access to diverse learning experiences, mentorship, and educational allowances to advance your skills in data engineering and related fields. Additionally, you will have opportunities to work on high-impact projects that can further refine your expertise and expand your professional repertoire.

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What is the company culture like at Wave for a Data Engineer?

The company culture at Wave is built on respect, inclusion, and the celebration of diverse viewpoints. As a Data Engineer, you will find an environment that fosters collaboration and innovation. Employees are encouraged to bring their authentic selves to work, ensuring that everyone's ideas are valued, regardless of their position. This inclusive culture has earned Wave recognition in Canada as one of the top workplaces for technology and mental health.

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What challenges might a Data Engineer face at Wave?

Data Engineers at Wave might encounter challenges like managing complex data systems and ensuring the reliability of the data infrastructure. Balancing innovative approaches with operational stability while transitioning from legacy systems to modern data solutions can require creative solutions. However, with strong support from a collaborative team and ample resources, Data Engineers can thrive and turn these challenges into opportunities for growth.

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Common Interview Questions for Data Engineer
Can you describe your experience with building data pipelines?

When asked about building data pipelines, focus on specific projects where you've designed or implemented data ingestion processes. Highlight the tools and technologies you used, such as AWS services, Python scripts, or industry-standard libraries. Be sure to explain how you ensured data quality and integrity throughout the pipeline.

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How do you ensure the reliability and availability of data systems?

To answer this question effectively, discuss your experience with data monitoring tools and incident-management protocols. Emphasize the importance of proactive monitoring, logging, and alerting to catch issues before they impact users, and provide an example of a time you successfully mitigated an incident.

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What is your experience with data lakes, and how have you built one?

When discussing your experience with data lakes, outline the steps you took to design and build a data lake using tools like Hudi or AWS S3. Include details about the data formats you worked with, such as Parquet or Avro, and any challenges you faced during the process, along with how you overcame them.

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Describe a time when you improved a data workflow.

In answering this question, specify a scenario where you identified inefficiencies in a data workflow. Discuss the methods and tools you employed to bring about improvement, including how you measured success and the overall impact on the organization.

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How do you handle working with cross-functional teams?

Talk about your collaborative experiences with cross-functional teams. Mention specific instances where you worked with stakeholders from analytics, IT, or product management to deliver data-driven solutions. Emphasize your communication skills and how you've built strong relationships with team members.

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What tools do you use for data governance and quality management?

When discussing data governance, mention tools like Apache Atlas, Collibra, or any cataloging solutions you've worked with. Explain how you've applied data quality practices and maintained data lineage in your previous roles, thereby ensuring compliance and discoverability.

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How do you approach troubleshooting data issues?

To answer this, describe your systematic approach to troubleshooting data issues, highlighting your debugging techniques and how you gather system logs and metrics. Present a specific case where your troubleshooting efforts led to a resolution and improved processes.

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Can you explain your coding process when developing data solutions?

Focus on your coding practices as a Data Engineer, including the importance of writing maintainable and clean code. Discuss your preference for version control, code reviews, and the use of CI/CD practices to deploy your solutions efficiently and reliably.

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What experience do you have with machine learning tools that integrate with data systems?

Share any relevant experience you have with machine learning platforms such as Amazon SageMaker or Azure ML, detailing how you've integrated these systems with your data pipelines. Provide examples where applicable of how this has added value to analytical processes or products.

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How do you stay up to date with the latest trends in data engineering?

In your response, mention your strategies for staying current with industry trends, such as attending conferences, participating in webinars, or contributing to open-source projects. Discuss specific resources, blogs, or websites you follow to enhance your knowledge and skill set in data engineering.

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Founded in 2010 and headquartered in Toronto, Ontario, Wave Apps provides software solutions and related services for small business owners to manage finances.

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Full-time, hybrid
DATE POSTED
November 28, 2024

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