Senior Data Engineer, SharkNinja

• 5–8 years in data engineering; 2+ in a senior/lead role
• Expert in SQL and Python (or Scala) for data transformation at scale
• Deep experience with batch and streaming data processing
• Proven ability to design large-scale integration solutions, custom pipelines, and resilient data workflows
• Familiarity with observability platforms and data quality frameworks
• Strong understanding of cloud data platforms, preferably Snowflake or BigQuery
• Excellent communication, mentorship, and cross-functional collaboration skills

Desirable

• The Senior Data Engineer is a critical technical leader in our modern data ecosystem
• Reporting to the Director of Data Engineering or equivalent, this role designs and delivers high-scale data solutions, mentors junior engineers, and helps shape the architectural backbone of our analytics infrastructure
• From complex integration frameworks to real-time pipelines and testing systems, this role balances execution with engineering excellence to support fast, reliable, and trustworthy data delivery
• Integration Architecture
• Design scalable, enterprise-ready data integration architectures using both third-party tools and custom frameworks. Define patterns that align with a domain-driven, data mesh approach
• Custom Frameworks
• Develop reusable libraries and integration components to support reliable data ingestion where off-the-shelf tools fall short. Codify retry logic, state handling, and performance tuning
• Observability & Testing
• Implement monitoring and testing systems across the data lifecycle. Drive adoption of automated testing in CI/CD, and build alerts that preempt failure and degradation
• Real-Time Pipeline Development
• Lead the buildout of high-throughput pipelines supporting streaming and low-latency use cases. Address challenges like slowly changing dimensions, windowing, and fault tolerance
• Mentorship & Code Quality
• Mentor junior engineers through code reviews and technical coaching. Establish best practices in data modeling, testing, and maintainability that level up the entire team
• Cross-Functional Collaboration
• Translate business and analytics requirements into data products by working closely with analysts, data scientists, and stakeholders. Communicate complex logic with clarity
• Innovation & Improvement
• Lead proofs-of-concept for emerging technologies. Identify opportunities to reduce technical debt, improve performance, and shape the evolution of the data platform