Senior Engineering Manager - Data Platform

Salary Range: 220000 to 290000 (Currency: USD) (Pay period: per-year-salary)

About the Role:

We are looking for a Senior Engineering Manager to lead our Data Platform & Infrastructure team. This leader will own the strategy, execution, and evolution of the foundational data systems that power insights and our ecosystem.

You will lead a team of highly capable engineers responsible for building reliable, scalable, secure, and developer-friendly data infrastructure. This includes data ingestion and processing, data platforms, storage and compute, orchestration, observability, and the infrastructure that enables teams across the company to efficiently build and operate data-intensive products and applications.

This is a hands-on leadership role: you will set technical direction and organizational strategy while remaining close enough to the technology to make high-quality architectural decisions and guide the team through complex technical challenges.

Responsibilities:

Leadership & Team Management

Lead, recruit, and mentor an exceptional engineering team.

Direct data infrastructure strategy to drive business and product success.

Oversee resource allocation, including headcount and vendor budget planning.

Establish operational rhythms, goals, and execution frameworks aligned with core business outcomes.

Cultivate an inclusive environment focused on ownership, technical rigor, and continuous professional development.

Champion team morale, engagement, learning opportunities, and delivery velocity.

Data Engineering Strategy

Guide system architecture, data flow design, quality standards, and platform roadmaps.

Partner with Product and Data leadership to plan, sequence, and execute multi-quarter initiatives.

Define robust platform abstractions, APIs, and SLAs to streamline developer workflows and increase adoption.

Ensure infrastructure resilience through comprehensive monitoring, incident management, disaster recovery, and cost optimization.

Work cross-functionally with Security, Audit, and Analytics Engineering to deploy key capabilities and compliance controls.

Cross-Functional Partnership

Engage with Data, Product, and Finance partners to enable machine learning, experimentation, and business reporting.

Establish and enforce organization-wide standards for pipeline reliability, data security, and quality.

Technical Leadership

Advocate for automation, engineering best practices, and thoughtful trade-offs between execution speed and long-term scalability.

Thrive in fast-paced, ambiguous settings while cultivating senior technical leaders and building leadership succession through delegated autonomy, developing senior technical leaders and succession depth.

Requirements:

Required Qualifications

10+ years of experience in software engineering, SRE or infrastructure, data platform engineering, or related fields.

4+ years managing high-performing technical teams, growing and developing people

Deep expertise with Snowflake/Google BigQuery/Databricks or equivalent modern data warehousing systems and ecosystem of tools including Airflow, DBT etc.

Familiarity with Git and CI/CD workflows 

Strong understanding of data warehousing concepts and infrastructure workload management.

Experience defining data security, platform SLAs and governance frameworks.

Excellent stakeholder management and communication skills.

Demonstrated ability to influence technical strategy across multiple organizations.

Preferred Qualifications

Experience delivering customer-facing data products is a plus

Experience scaling platform organizations through rapid company growth.

Familiarity with event-driven data architectures, data science & product analytics computational needs and patterns is a plus.

Experience supporting experimentation platforms and machine learning initiatives.

Knowledge of metrics governance and executive KPI frameworks.

Success in your First Year:

Within your first 12 months, you will:

Build strong relationships with leaders across Product, Engineering, Finance, and Go-to-Market.

Improve trust in data through stronger governance and controls.

Increase data consumptions and data-powered products across the organization.

Establish scalable development standards and improve engineering velocity.

Grow and mentor a high-performing data platform engineering team.

Deliver a roadmap that supports both current business needs and future scale.