Senior Data Engineer (7-month Contract)

As a Senior Data Engineer at StackAdapt, you’ll be responsible for the data architecture and engineering within our Data Lake and Enterprise Data Warehouse. This includes building reliable data ingestion pipelines from multiple sources, orchestrating these pipelines to ensure seamless data processing, and collaborating with BI engineers to meet stakeholders' business intelligence needs. You'll also monitor and maintain the day-to-day operations of our data pipelines.

To qualify for the Senior Data Engineer role at StackAdapt, candidates should have a minimum of 5 years of experience in data engineering within an Enterprise Data Warehouse environment. Experience with cloud-based data warehouses like Snowflake, big data mediums, and managing cloud infrastructure on AWS/AZURE/GCP is essential. Knowledge of data warehousing architecture and hands-on experience with ETL/ELT data pipelines is also required.

At StackAdapt, we prioritize the professional development of our Senior Data Engineers by providing opportunities for continuous learning and collaboration. As part of our dynamic Enterprise Data Office, you’ll work alongside experts in the field to tackle challenging projects and develop your skills further, all within a supportive and inclusive environment.

In your role as a Senior Data Engineer at StackAdapt, you'll work with a variety of tools and technologies including cloud-based data warehouses like Snowflake, orchestration tools like Apache Airflow, and languages such as Python and SQL. Familiarity with Infrastructure-as-Code via Terraform and container technologies like Kubernetes will also be beneficial as you conduct data operations.

Yes! StackAdapt is a Remote-First company, meaning that the Senior Data Engineer position is open to candidates located anywhere in Canada. We value flexibility and strive to create a work environment that accommodates diverse needs.

When answering this question, provide specific examples of the data ingestion pipelines you've built in previous roles, detailing the technologies used and the challenges faced. Highlight how your contributions improved data quality or efficiency within the organization.

Discuss your methodology when designing data architecture, mentioning considerations like scalability, performance, and security. Share specific frameworks or design patterns you've employed and how they best served the business needs.

Focus on your strategies for maintaining data integrity and quality, such as validation checks, error handling, and logging practices. Provide examples of how you've implemented these strategies in past projects to enhance data reliability.

Here, detail your experience with cloud platforms like AWS, Azure, or GCP. Mention specific services such as data warehouses (e.g., Snowflake) and how these platforms integrate with your data engineering processes.

Provide an overview of the ETL/ELT tools you have utilized, such as Apache NiFi, Talend, or Spark. Discuss how you've leveraged these tools to streamline data workflows and achieve faster insights for your stakeholders.

Explain your troubleshooting process and how you monitor data pipelines for alerts. Share examples of past production issues you successfully resolved, emphasizing your analytical skills and problem-solving abilities.

Share a specific instance where you worked closely with BI Engineers to fulfill reporting needs. Discuss the challenges faced, how you collaborated to overcome them, and the positive outcomes of that teamwork.

Discuss the data modeling techniques you are familiar with, such as Kimball or Inmon methodology. Explain why you prefer certain techniques and provide an example of a project where you employed these methods effectively.

Highlight your commitment to continuous learning through professional development activities, such as attending workshops, following industry publications, or participating in online courses. Mention any specific resources you find helpful.

Share your perspective on current challenges such as data privacy regulations, the need for real-time data processing, or data integration complexities. Discuss how you believe these challenges can be addressed within the industry.