Job Description

We are looking for a Senior Engineering Manager, Data & AI to take ownership of how data is structured, leveraged, and evolved across the company.

This role goes beyond leading a team, you will be responsible for understanding the current state of data across multiple products, identifying gaps, and defining the path forward to make data more reliable, accessible, and valuable for the business.

You will operate in a high-autonomy environment, working directly with the CTO and leadership team to shape priorities, recommend investments, and define how data and AI should support the company’s growth and product strategy.

This is a hands-on leadership role (approximately 50/50), where you are expected to stay close to the technical details while also making informed decisions at a strategic level. You should be comfortable quickly diagnosing problems, forming a point of view, and driving execution without relying heavily on layers of delegation.

You will lead a globally distributed team of data engineers (currently 6 ICs) and play a key role in building and scaling this function over time, including influencing hiring decisions, team structure, and ways of working.

This role requires someone who can connect data, technology, and business priorities, understanding what matters most for the company, where to invest, and how to translate data into meaningful impact across product, operations, and decision-making.

We are looking for a pragmatic builder mindset: someone who is grounded in reality, able to navigate ambiguity, and capable of evolving both the data platform and the company’s approach to AI as the landscape continues to change.

This role also represents a high-impact growth opportunity, offering strong exposure to strategic decision-making and business priorities. For someone who delivers meaningful results, demonstrates strong business understanding, and takes ownership, this position can naturally evolve into broader leadership responsibilities as the business scales.

Job requirements

Experience

• Around 10+ years of experience in data engineering or related fields

• Proven leadership experience, ideally around 4+ years, with enough maturity to lead senior engineers and earn their respect

• Experience leading individual contributors; prior experience managing managers is not required

• Strong hands-on experience with modern data platforms such as Snowflake (preferred), Databricks, or similar technologies

• Solid experience working with cloud-based environments (AWS preferred, but other cloud platforms are acceptable if there is strong ability to adapt quickly)

• Practical experience working with modern data engineering ecosystems, with exposure to AI/ML and GenAI - without reliance on buzzwords or superficial expertise

• Experience working in globally distributed teams and collaborating across functions

Skills

• Strong expertise in data engineering fundamentals: data warehousing, pipelines, data modeling, and scalable data systems

• Ability to work across different tools and technologies, with a focus on understanding concepts rather than relying on specific stacks

• Strong technical judgment, with the ability to quickly understand issues, propose solutions, and make informed decisions without excessive dependency on others

• Ability to balance hands-on execution with strategic thinking (approximately 50/50)

• Strong understanding of how data supports business priorities, product decisions, and operational needs

• Clear and effective communication, with the ability to interact confidently with both technical teams and leadership

• Pragmatic and grounded mindset, able to navigate ambiguity, avoid overengineering, and focus on what actually drives impact

• Curiosity and adaptability to keep up with the rapid evolution of data engineering and AI, with a critical eye to distinguish real value from hype

• A strong get-things-done mindset, with the ability to move quickly from problem identification to practical solutions and execution

Job responsibilities

• Leadership and strategy:

• Define and evolve the strategy for data engineering and AI platforms in alignment with business goals and product priorities

• Act as a key partner to leadership, bringing a clear point of view on where to invest, what to prioritize, and how to evolve the data ecosystem

• Assess the current state of data across the company and define a pragmatic roadmap to improve usability, reliability, and impact

• Drive innovation in data and AI with a practical and execution-focused approach, balancing long-term vision with immediate needs

Data Engineering & Platform Development:

• Lead the design, development, and operation of scalable data platforms (e.g., Snowflake, data lakes, streaming systems)

• Stay close to the technical details, supporting the team in solving complex problems and making informed decisions

• Ensure data pipelines and systems are reliable, performant, and aligned with business use cases

• Take ownership of technical decisions and trade-offs, moving quickly from problem identification to solution

Data Warehousing & Analytics Enablement:

• Oversee and improve the current data warehouse and transformation layers (Snowflake preferred)

• Enable consistent, well-structured, and accessible data across the organization

• Partner closely with BI teams to support reporting, experimentation, and product insights

• Improve data quality, discoverability, and usability for both technical and non-technical stakeholders

• Drive initiatives that reduce time-to-insight and increase the practical value of data across teams

AI & Advanced Analytics:

• Identify and drive practical applications of AI/ML, GenAI, and LLMs based on real business needs

• Partner with product and engineering teams to integrate AI capabilities into internal and customer-facing solutions

• Evaluate opportunities critically, focusing on real impact rather than trends or hype

Collaboration & Global Partnership:

• Collaborate with engineering, product, BI, and business teams across multiple regions

• Act as a central point of ownership for data across the organization, supporting multiple products and departments

• Align stakeholders on priorities, trade-offs, and execution plans

Operational Excellence:

• Improve data reliability, scalability, and cost-efficiency across systems

• Drive automation and enable self-service capabilities where appropriate

• Contribute to data governance practices in partnership with other teams (e.g., BI, Trust & Safety)

Talent Development & Culture:

• Lead, mentor, and grow a high-performing team of data engineers

• Play a key role in shaping the team structure, hiring strategy, and future organization

• Foster a culture of ownership, execution, and continuous improvement