About the role
At Sparq, we help companies solve the right problems—not just build more technology.
We’re a modern product engineering partner blending strategy, craftsmanship, and speed to help organizations modernize confidently in the age of AI. From data ecosystems to digital products and AI acceleration, we turn complexity into clarity and ideas into impact.
If you’re driven to build what’s next, lead with empathy, and deliver excellence without ego, you’ll feel right at home at Sparq.
Why You’ll Love This Role:
Work with cutting-edge cloud data technologies in a dynamic, collaborative environment
Tackle enterprise-scale data challenges, working with billions of rows of data
Opportunities for career growth and skill development through mentorship and certification programs
Fully remote work flexibility
About the Role:
We are seeking a Senior Data Engineer with expertise in Snowflake and dbt, with a strong focus on scalability and optimization. The ideal candidate has experience working with massive datasets at the enterprise level and can fine-tune and optimize Snowflake environments to enhance performance, cost efficiency, and best practices.
Responsibilities:
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, ensuring optimized transformations and modeling
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
What You Bring:
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
Bonus Points for:
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.)
Regardless of your specific role, we seek individuals who are excited to explore, adopt, and evangelize AI tools and methodologies. If you have experience in AI or a proven track record of rapidly learning and mentoring others on emerging tech, you’ll fit right in.
Equal Employment Opportunity Policy: Sparq is proud to offer equal employment opportunity without regard to age, color, disability, gender, gender identity, genetic information, marital status, military status, national origin, race, religion, sexual orientation, veteran status, or any other legally protected characteristic.
We are committed to providing equal employment opportunities and believe in an inclusive workplace. If you require reasonable accommodations to participate in the job application or interview process, please let us know by contacting [email protected]
C2C is not available
Originally posted on Himalayas