Sr. Data Engineer (Snowflake/dbt)
WFA Digital Insight
As demand for cloud-based data solutions continues to soar, with over 70% of companies adopting cloud-based data warehousing, the need for skilled data engineers has never been more pressing. With the global data engineering market projected to grow by 25% annually, professionals with expertise in Snowflake and dbt are in high demand. Sparq, a modern product engineering partner, is seeking a Senior Data Engineer to join their team, offering a unique chance to work on enterprise-scale data challenges and drive innovation in the field. Before applying, candidates should be aware that this role requires strong technical leadership and a deep understanding of data governance and security best practices.
Job Description
About the Role
The Senior Data Engineer position at Sparq is a critical role that involves designing, building, and optimizing scalable data pipelines using Snowflake and dbt. As a key member of the data engineering team, you will work closely with cross-functional teams to understand business requirements and develop high-performance data solutions. Your expertise will be instrumental in migrating and refining legacy data processes, ensuring they are optimized for performance, cost efficiency, and best practices.The day-to-day responsibilities of this role are diverse and challenging, requiring a strong balance of technical skills, collaboration, and problem-solving. You will lead efforts in optimizing Snowflake environments, troubleshoot bottlenecks in existing workflows, and provide technical guidance to ensure the team follows best practices for scalable data engineering.
Sparq is committed to fostering a dynamic, collaborative environment where innovation and empathy thrive. As a Senior Data Engineer, you will be part of a team that values excellence, continuous learning, and the delivery of impactful solutions.
What You Will Do
- Design and build scalable data pipelines in Snowflake and dbt, ensuring they can handle billions of rows of data efficiently
- Optimize Snowflake storage, compute performance, and query execution to improve processing speed and cost efficiency
- Lead efforts in migrating and refining legacy data processes in Snowflake using dbt
- Collaborate with business and data teams to understand requirements and translate them into high-performance data solutions
- Implement best practices for Snowflake optimization, including clustering, partitioning, indexing, materialized views, and workload management
- Troubleshoot and resolve bottlenecks in existing Snowflake-based ETL/ELT workflows
- Provide technical leadership and mentorship, ensuring the team follows best practices for scalable data engineering
- Create and maintain technical documentation, including architecture diagrams and optimization guidelines
- Work with cutting-edge cloud data technologies in a dynamic, collaborative environment
- Tackle enterprise-scale data challenges, working with billions of rows of data
What We Are Looking For
- 3+ years of experience in data engineering, with a focus on cloud-based enterprise-scale data solutions
- Proven experience working with massive datasets (billions of rows) in Snowflake
- Hands-on expertise in Snowflake performance tuning, storage optimization, and cost management
- Deep experience with dbt for data transformation, testing, and workflow orchestration
- Strong proficiency in SQL and Python for data manipulation, automation, and optimization
- Ability to identify, diagnose, and optimize inefficient queries and processing workflows
- Experience working both with and without an architect to optimize Snowflake performance
- Strong understanding of data governance, security best practices, and role-based access control in Snowflake
- Excellent problem-solving and communication skills, with the ability to collaborate across teams
Nice to Have
- Experience with orchestration tools like Airflow or Prefect
- Exposure to AWS, GCP, or Azure for cloud data integration
- Familiarity with streaming data pipelines (Kafka, Kinesis, etc.)
Benefits and Perks
- Fully remote work flexibility
- Opportunities for career growth and skill development through mentorship and certification programs
- Collaborative and dynamic work environment
- Chance to work on cutting-edge cloud data technologies and enterprise-scale data challenges
- Access to a community of professionals passionate about innovation and excellence
- Comprehensive benefits package, including health insurance and retirement planning
- Flexible PTO policy to ensure work-life balance
How to Stand Out
- Familiarize yourself with Sparq's approach to product engineering and data solutions to understand their specific needs and challenges.
- Showcase your experience with Snowflake and dbt by providing specific examples of how you've optimized data pipelines and led data process migrations in previous roles.
- Highlight your ability to work collaboratively in a remote setting and your experience with agile methodologies.
- Prepare to discuss your approach to data governance, security, and compliance, as these are critical aspects of the role.
- Consider creating a portfolio that demonstrates your technical skills and experience with data engineering projects, including any certifications or training you've completed.
- Be prepared to discuss your salary expectations and how they align with industry standards for senior data engineers.
- Look for red flags such as unclear expectations, lack of communication, or unrealistic demands during the interview process.
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