Sr. Data Engineer (Snowflake/dbt)

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 environmentTackle enterprise-scale data challenges, working with billions of rows of dataOpportunities for career growth and skill development through mentorship and certification programsFully remote work flexibilityAbout 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 efficientlyOptimize Snowflake storage, compute performance, and query execution to improve processing speed and cost efficiencyLead efforts in migrating and refining legacy data processes in Snowflake using dbt, ensuring optimized transformations and modelingCollaborate with business and data teams to understand requirements and translate them into high-performance data solutionsImplement best practices for Snowflake optimization, including clustering, partitioning, indexing, materialized views, and workload managementTroubleshoot and resolve bottlenecks in existing Snowflake-based ETL/ELT workflowsProvide technical leadership and mentorship, ensuring the team follows best practices for scalable data engineeringCreate and maintain technical documentation, including architecture diagrams and optimization guidelinesWhat You Bring:3+ years of experience in data engineering, with a focus on cloud-based enterprise-scale data solutionsProven experience working with massive datasets (billions of rows) in SnowflakeHands-on expertise in Snowflake performance tuning, storage optimization, and cost managementDeep experience with dbt for data transformation, testing, and workflow orchestrationStrong proficiency in SQL and Python for data manipulation, automation, and optimizationAbility to identify, diagnose, and optimize inefficient queries and processing workflowsExperience working both with and without an architect to optimize Snowflake performanceStrong understanding of data governance, security best practices, and role-based access control in SnowflakeExcellent problem-solving and communication skills, with the ability to collaborate across teamsBonus Points for:Experience with orchestration tools like Airflow or PrefectExposure to AWS, GCP, or Azure for cloud data integrationFamiliarity 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 recruiting@teamsparq.comC2C is not availableOriginally posted on Himalayas