About the Role
We are seeking a Senior Manager of Data & Analytics Engineering to lead our data platform teams and power decision-making across the company. In this senior leadership position, you will own and evolve our end-to-end data platform-from ingestion and transformation to analytics layers that business teams rely on daily. You'll oversee Data Engineering (infrastructure, pipelines, reliability) and Analytics Engineering (data models, metrics, self-serve tooling), while championing an AI-first approach to the way we build, operate, and innovate.
Four Pillars of This Role
- Platform Leadership: Own the architecture and roadmap for the modern data stack, from source systems through to consumption layers.
- Team Building: Hire, grow, and inspire both data engineers and analytics engineers, fostering a culture of quality, curiosity, and ownership.
- AI Integration: Embed AI tooling natively into the team's workflows for build, testing, documentation, and monitoring of our data platform.
- Business Partnership: Translate commercial priorities into robust data infrastructure that is agile, trusted, and scalable.
What you will do:
- Define and own the multi-year roadmap for the data platform, aligning investments in infrastructure, tooling, and headcount with business strategy.
- Lead and grow the Data and Analytics team, cultivating a collaborative, feedback-rich environment with clear career pathways.
- Architect and oversee scalable data pipelines across ingestion, transformation, orchestration, and delivery, for both batch and streaming use cases.
- Champion best practices in analytics engineering, including semantic layer design, dbt modelling standards, data contracts, and metrics governance.
- Partner with business stakeholders to deliver high-quality, self-serve data solutions aligned to business needs.
- Ensure data platform reliability, observability, SLAs, and incident response, treating the platform as a product with real users.
- Drive vendor and tool evaluations for the modern data stack (cloud warehouse, orchestration, cataloging, transformation, reverse ETL, etc.).
- Set and enforce data quality, documentation, and governance standards to build trust across the business.
- AI-assisted development: Champion use of AI coding assistants and LLM-powered tooling (e.g. Cursor, GitHub Copilot, Claude) to accelerate delivery and reduce toil.
- Intelligent data pipelines: Implement AI-native patterns-LLM-generated documentation, anomaly detection, data quality monitoring, and automated root-cause analysis.
- Natural language interfaces: Prototype NL-to-SQL and AI-powered BI tools to empower self-serve analytics for non-technical users.
- AI platform enablement: Build foundational data infrastructure (feature stores, vector stores, model metadata, evaluation datasets) to enable AI and ML experimentation and scale.
What you'll need to know/have:
- 7+ years in data engineering or analytics engineering, with 3+ years in a senior leadership role managing multiple teams
- Deep expertise in the modern data stack-cloud data warehouses (Snowflake, BigQuery, or Databricks), dbt, orchestration tools (Airflow, Dagster, or Prefect), and ELT frameworks
- Strong command of SQL and Python
- Hands-on experience integrating AI/LLM tooling into engineering workflows or data products
- Proven ability to define and execute a multi-year data platform strategy
- Strong stakeholder management, including executive presentations and translating technical concepts to non-technical audiences
- Experience building and scaling high-performing engineering teams: hiring, mentoring, performance management
- Track record of delivering trusted, well-documented, and widely adopted data products
It would be great if you also had:
- Familiarity with semantic layer tools (e.g. MetricFlow, Cube), data cataloging (e.g. Atlan, Datahub), and data observability platforms
- Experience with streaming data (Kafka, Flink, or Kinesis) and batch processing
- Exposure to data mesh or data product organizational models
We want to increase representation of all races, genders, and body types in the cycling industry and are committed to building a diverse and inclusive workforce where all people thrive. We encourage everyone – especially those from marginalized groups – to apply to our job postings and help us earn the position as the rider’s brand of choice. We are always looking for creative, innovative, and passionate people who are eager to contribute to our mission of pedaling the planet forward. Regardless of your qualifications, if you are ready to make a difference, please apply and let us know how you can make an impact at Specialized!
See what we are up to on LinkedIn, Instagram.
Skills Required
- 7+ years in data engineering or analytics engineering
- 3+ years in a senior leadership role managing multiple teams
- Deep expertise with cloud data warehouses (Snowflake, BigQuery, or Databricks)
- Experience with dbt and analytics engineering best practices
- Experience with orchestration tools (Airflow, Dagster, or Prefect)
- Strong command of SQL
- Strong command of Python
- Hands-on experience integrating AI/LLM tooling into engineering workflows or data products
- Proven ability to define and execute a multi-year data platform strategy
- Stakeholder management and executive communication skills
- Experience building and scaling engineering teams: hiring, mentoring, performance management
- Track record delivering trusted, well-documented, widely adopted data products
- Familiarity with semantic layer tools (e.g., MetricFlow, Cube)
- Familiarity with data cataloging tools (e.g., Atlan, DataHub)
- Familiarity with data observability platforms
- Experience with streaming technologies (Kafka, Flink, or Kinesis)
- Exposure to data mesh or data product organizational models
Specialized Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Specialized and has not been reviewed or approved by Specialized.
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Retirement Support — A 401(k) with Fidelity offers up to $5,000 in annual employer matching with immediate vesting, and the company covers plan administrative fees for active employees. This provides strong retirement support within the total package.
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Parental & Family Support — Published materials describe 6–8 weeks at 100% pay for birthing recovery and 8 weeks of fully paid parental bonding, alongside fertility benefits and adoption assistance. Some materials indicate certain roles or locations may offer even longer fully paid leave.
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Wellbeing & Lifestyle Benefits — Cycling‑centric perks include substantial gear discounts, race entry fee support, on‑site fitness options, healthy on‑site meals, an employee bike service center, and a dog‑friendly HQ. These benefits can meaningfully enhance day‑to‑day experience for those who value the cycling culture.
Specialized Insights
What We Do
Founded on the principle of performance and fueled by innovation, our focus on the rider and their needs is our constant. From seasoned pros and weekend warriors, to kids and commuters—if you ride, we’re for you. The passion we have for sharing our love of bikes with the world can be seen in those who choose to work here. Headquartered in Morgan Hill, CA, the passionate and creative teammates at Specialized have designed and manufactured the world's most innovative bikes and gear since 1974. Are you ready to join our team and help pedal the planet forward?
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