Senior Manager - Data Engineering

About the role

You will define and drive the technical vision for our cloud data platform, ensuring that data models remain robust, scalable, and aligned with evolving business needs. You will lead a team of data engineers, mentoring them in best practices while fostering a culture of quality, ownership, and continuous improvement. You will act as a key technical partner to analytics, product, and operations, translating ambiguous requirements into reliable data solutions. You will raise the standard of our dbt modeling layer, enforcing conventions that make our assets understandable and reusable. You will own the roadmap for our Snowflake and Azure infrastructure, balancing innovation with operational stability. You will establish governance processes that enable the platform to scale safely as the organization grows. You will champion data observability and testing to reduce risk and increase confidence in production pipelines. You will communicate complex technical concepts clearly to both technical and non-technical audiences, ensuring alignment across the business.

Key facts

What you'll do

Architect and evolve our Snowflake data platform, defining storage, processing, and security strategies that support current and future analytical needs.

Lead the development lifecycle for dbt projects, guiding code review standards, testing strategy, and documentation expectations for the team.

Partner with analytics and product teams to design new data domains, turning raw source systems into curated, semantic models.

Establish and refine data modeling conventions, ensuring consistency in naming, grain, and documentation across all datasets.

Collaborate with stakeholders to gather requirements, validate data outputs, and prioritize backlog items that unblock or empower the organization.

Implement monitoring and observability practices that surface pipeline health, data quality issues, and performance bottlenecks early.

Mentor data engineers through code reviews, technical guidance, and career development, helping them grow mastery in cloud and modeling tools.

Define and manage operational processes, including deployment workflows, environment strategy, and incident response for data pipelines.

Evaluate emerging tools and patterns in the data space, recommending pragmatic improvements that align with our architecture and team skills.

Own the end-to-end delivery of data initiatives, coordinating timelines, dependencies, and communication to ensure successful outcomes.

Drive data quality frameworks and testing coverage, reducing manual troubleshooting and increasing trust in analytical results.

Represent the data platform in cross-functional discussions, advocating for sustainable practices and long-term technical health.

Track key platform metrics, such as pipeline reliability and user satisfaction, and use insights to guide incremental improvements.

Work closely with leadership to translate business objectives into technical priorities, ensuring the data stack supports measurable outcomes.

Requirements

You possess a Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

You bring 8+ years of professional experience in data engineering, data platform, or analytics infrastructure roles.

You have 3+ years of hands-on experience with Snowflake, including performance tuning, storage optimization, and security features.

You have deep experience with dbt, including version control workflows, package management, and incremental modeling strategies.

You are proficient in Azure services relevant to data pipelines, including storage, compute, and identity management.

You have a strong track record of building and leading engineering teams, including hiring, mentoring, and performance development.

You demonstrate mastery of SQL and data modeling techniques, including dimensional modeling, normalization tradeoffs, and partitioning strategies.

You are comfortable working in fast-paced, ambiguous environments while maintaining a disciplined approach to quality and documentation.

Nice to have

Experience with data observability tools and frameworks for monitoring pipeline health and data quality.

Familiarity with Python or other programming languages for data engineering workflows and automation.

Background in consulting or client-facing roles where clear communication with stakeholders is essential.

Practical notes

This is a full-time remote role.

Our team operates in a synchronous and asynchronous manner, requiring availability during overlapping business hours.

Successful candidates must be eligible to work in the country where the role is based without sponsorship at this time.