- Location
- Uruguay, Montevideo (Office)
- Type
- Full-time
- Seniority
- Manager
- Source
- Workday
Description
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!
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