Director, Data Engineering

SharkNinja•Needham, MA

1d•$175,500 - $235,000•Onsite

About The Position

The Digital Transformation organization at SharkNinja is responsible for accelerating the company's evolution through modern technology, trusted data, intelligent automation, and scalable digital capabilities. We partner across the enterprise to transform how we innovate, operate, and make decisions—enabling faster execution, deeper consumer insights, and sustainable growth. As Director, Data Engineering, you will lead the strategy, architecture, and execution of SharkNinja's enterprise data engineering ecosystem. You will build and develop a high-performing team responsible for delivering reliable, scalable, and secure data platforms that power analytics, AI, machine learning, operational reporting, and digital products across the business. This is a highly visible leadership role requiring a strategic thinker who thrives in a fast-paced environment and is passionate about building modern data platforms that create measurable business value. You will partner closely with Digital Transformation, Product Development, Commercial, Supply Chain, Finance, Customer Experience, and Enterprise Technology leaders to ensure trusted data enables faster decisions, operational excellence, and continued innovation. Success in this role requires someone who is Rarely Satisfied, embraces Progress Over Perfection, understands that Details Make the Difference, believes Winning is a Team Sport, and consistently Communicates for Impact while delivering scalable technology solutions that positively impact our business and our consumers.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or a related technical discipline.
10+ years of progressive experience in Data Engineering, Data Platform Engineering, Software Engineering, or related technical fields.
Significant experience leading and developing high-performing engineering teams.
Demonstrated success delivering enterprise-scale cloud data platform implementations.
Experience designing and supporting modern enterprise data architectures in complex, global organizations.
Expertise with cloud platforms including Microsoft Azure, AWS, or Google Cloud Platform.
Deep experience with modern data technologies including: Microsoft Fabric, Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, Databricks, Snowflake, Microsoft SQL Server
Advanced proficiency with SQL, Python, Spark, distributed data processing, and modern ELT/ETL architectures.
Experience with orchestration platforms such as Azure Data Factory Pipelines, Apache Airflow, or equivalent.
Strong understanding of DataOps practices including CI/CD, Infrastructure as Code (Terraform, Bicep, or similar), automated testing, and deployment automation.
Experience implementing enterprise data governance, metadata management, master data management, data cataloging, lineage, and data quality frameworks.
Knowledge of event-driven architectures, APIs, Kafka, and streaming data platforms.
Familiarity with AI/ML data pipelines, feature engineering, and model enablement.
Experience supporting enterprise BI platforms such as Power BI, Tableau, or equivalent analytics tools.
A passion for building and developing exceptional engineering teams.
Strategic thinking combined with a strong bias toward execution.
Exceptional communication skills with the ability to translate complex technical concepts for executive and business audiences.
Outstanding collaboration and stakeholder management skills across business and technology organizations.
Comfort navigating ambiguity while driving alignment and delivering results.
A continuous improvement mindset with a relentless focus on engineering excellence.
A commitment to building scalable, secure, and resilient data platforms that accelerate innovation across SharkNinja.
Leadership behaviors aligned with SharkNinja's Success Drivers: Rarely Satisfied, Progress Over Perfection, Details Make the Difference, Winning is a Team Sport, Communicating for Impact

Nice To Haves

Master's degree in Computer Science, Engineering, Information Systems, or a related discipline.
Experience within consumer products, retail, manufacturing, or digital commerce organizations.
Experience leading enterprise digital transformation initiatives.
Experience supporting AI and Machine Learning platforms at scale.

Responsibilities

Define and execute the enterprise Data Engineering strategy in alignment with SharkNinja's Digital Transformation roadmap.
Build, lead, coach, and develop a high-performing organization of Data Engineers and technical leaders while fostering a culture of ownership, innovation, collaboration, and continuous improvement.
Architect, implement, and evolve modern cloud-based data platforms that support enterprise analytics, AI/ML initiatives, operational reporting, and self-service analytics.
Establish engineering standards and best practices for data architecture, ingestion, transformation, orchestration, observability, governance, metadata, lineage, and data quality.
Lead the modernization of legacy data environments into scalable, cloud-native architectures that improve reliability, performance, scalability, and cost efficiency.
Partner with business and technology leaders to translate strategic priorities into enterprise data solutions that accelerate decision-making and operational excellence.
Collaborate with Analytics, Data Science, AI, Enterprise Applications, Infrastructure, and Cybersecurity teams to deliver integrated, secure, and governed data products.
Drive adoption of DataOps best practices, including CI/CD, Infrastructure as Code, automated testing, monitoring, and deployment automation.
Define service level objectives and operational metrics to ensure platform reliability, performance, availability, and engineering excellence.
Champion enterprise data governance initiatives while ensuring compliance with security, privacy, and regulatory requirements.
Evaluate emerging technologies and identify opportunities to improve SharkNinja's data ecosystem through innovation, automation, and continuous optimization.
Lead architecture reviews and provide technical direction for complex enterprise initiatives involving structured, semi-structured, streaming, and real-time data.
Manage vendor relationships, technology investments, engineering roadmaps, and departmental budgets to maximize business value and return on investment.
Present platform strategy, engineering progress, technology roadmaps, and key risks to executive leadership while influencing enterprise technology direction.
Foster an environment that encourages experimentation, learning, accountability, and the pursuit of Outrageously Extraordinary results.