Sr. Manager, Data & Analytics

Responsibilities

• 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
• See what we are up to on LinkedIn , Instagram.

Additional Information

Sr. Manager, Data & Analytics 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.

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