Engineering Manager, Data Engineering

at  Grammarly Inc

Grammarly is excited to offer a remote-first hybrid working model. Grammarly team members in this role must be based in Germany, and, depending on business needs, they must meet in person for collaboration weeks, traveling if necessary to the hub(s) where their team is based.
This flexible approach gives team members the best of both worlds: plenty of focus time along with in-person collaboration that fosters trust and unlocks creativity.

THE OPPORTUNITY

To achieve our ambitious objectives, we are seeking an Engineering Manager to lead, scale, mentor, and drive our Data Engineering Team. This individual will be responsible for guiding a skilled team of software engineers who are focused on constructing analytical data models, pipelines, and innovative tools. They will collaborate with various teams across the company.
This person will be an integral part of the larger data organization, reporting directly to the Director of Data Engineering based in the US, and they’ll have the opportunity to influence decisions and the direction of our overall data platform, including infrastructure and analytics engineering.
Grammarly’s engineers and researchers have the freedom to innovate and uncover breakthroughs—and, in turn, influence our product roadmap. The complexity of our technical challenges is growing rapidly as we scale our interfaces, algorithms, and infrastructure. You can hear more from our team on our technical blog.
As the Manager of the Data Engineering team, you will co-own the company-wide data lake and set the vision for data schemas, analytical tooling, and pipelines. Our cutting-edge data lake is the central hub for all data producers and consumers, and your impact will span all of Grammarly.
Data engineers make disparate data centrally available to Grammarly’s team members while ensuring query efficiency and efficacy. Your team will support all of Grammarly’s functions to help us successfully operate three lines of business: individual consumers, enterprise, and Grammarly’’s developer platform. Communication, stakeholder management, and a passion for data will be the backbone of Grammarly’s Data Engineering Manager’s success.

In this role, you will:

• Build a highly specialized engineering team to support the growing needs and complexity of our product and business organizations.
• Oversee the maintenance and enhancement of our data lake, ensuring it meets the needs of all data producers and consumers.
• Drive the implementation of robust data pipelines to ensure data is accurate, accessible, and up-to-date.
• Foster a collaborative and high-performance culture within the team.
• Set technical vision for the team and ensure the scalability, low cost, low latency, and versatility of our internal analytics platform.
• Provide hands-on guidance in coding, design, and debugging to ensure high-quality technical solutions.
• Build strong relationships with all of Grammarly’s functions to generalize data needs and guide the roadmaps of our internal platforms.
• Work closely with partner teams, including Data Science, ML, and Analytics Engineering, to align priorities and deliver integrated solutions.
• Stay up-to-date with industry trends and best practices in data engineering, fostering a culture of continuous improvement and innovation.
• Cultivate an ownership mindset and culture on your team and across product teams: provide the necessary metrics to help us understand what is working, what is not, and how to fix it.
• Set high performance and quality standards and coach team members to meet them; mentor and grow junior and senior IC talent.

QUALIFICATIONS

• Has 7+ years of experience in data engineering, with at least 3 years in a leadership or managerial role.
• Fluent in data engineering technologies and languages like SQL, Python, Spark, and data pipeline frameworks.
• Demonstrates ability to lead and mentor a team. Strong project management skills and a track record of delivering complex projects.
• Has excellent analytical and problem-solving skills. Ability to dive deep into technical issues and provide effective solutions.
• Is able to guide the team to build optimal data models and efficiently utilize our data infrastructure.
• Has strong communication and interpersonal skills. Experience working in a collaborative, cross-functional environment, including with remote teams.
• Has the ability and desire to operate in a fast-paced, dynamic environment where things change quickly.
• Leads by setting well-understood goals and sharing the appropriate level of context for maximum autonomy but is also deeply technical and can dive in to help when necessary.
• Embodies our EAGER values—is ethical, adaptable, gritty, empathetic, and remarkable.
• Is inspired by our MOVE principles: move fast and learn faster; obsess about creating customer value; value impact over activity; and embrace healthy disagreement rooted in trust.
• Is able to meet in person for their team’s scheduled collaboration weeks, traveling if necessary to the hub where their team is based.

• Build a highly specialized engineering team to support the growing needs and complexity of our product and business organizations.
• Oversee the maintenance and enhancement of our data lake, ensuring it meets the needs of all data producers and consumers.
• Drive the implementation of robust data pipelines to ensure data is accurate, accessible, and up-to-date.
• Foster a collaborative and high-performance culture within the team.
• Set technical vision for the team and ensure the scalability, low cost, low latency, and versatility of our internal analytics platform.
• Provide hands-on guidance in coding, design, and debugging to ensure high-quality technical solutions.
• Build strong relationships with all of Grammarly’s functions to generalize data needs and guide the roadmaps of our internal platforms.
• Work closely with partner teams, including Data Science, ML, and Analytics Engineering, to align priorities and deliver integrated solutions.
• Stay up-to-date with industry trends and best practices in data engineering, fostering a culture of continuous improvement and innovation.
• Cultivate an ownership mindset and culture on your team and across product teams: provide the necessary metrics to help us understand what is working, what is not, and how to fix it.
• Set high performance and quality standards and coach team members to meet them; mentor and grow junior and senior IC talent