Senior Analytics Manager

Chalice Custom Algorithms

3d•$160,000 - $170,000

About The Position

This is an early analytics team hire with a mandate to raise the standard for how the business accesses, interprets, and acts on data. Working within a broader data and engineering organization, you will assess current analytical capabilities, partner with stakeholders to prioritize and deliver reporting solutions, and help define the team's standards for quality, tooling, and AI-assisted analytics work. The right candidate has strong SQL and BI fundamentals, hands-on Superset experience, and a genuine interest in building processes that scale. You are comfortable in stakeholder conversations and comfortable in a query editor. You default to documentation and repeatable standards over one-off fixes.

Requirements

5-7 years in analytics and BI: supporting business stakeholders and delivering reporting solutions in BI, analytics engineering, or data roles
Strong SQL: complex query authoring, window functions, CTEs, and performance-aware query construction for production reporting
Apache Superset: dashboard development, dataset configuration, user management, and working knowledge of the semantic layer
Snowflake: query development, data modelling for reporting, and working familiarity with warehouse operations
Data engineering fundamentals: ETL or ELT pipeline construction in support of analytical datasets, with an understanding of data quality concepts
Documentation discipline: requirements documents, stakeholder alignment outputs, and structured reporting on analytical findings
Applied GenAI in analytics: hands-on experience with at least one GenAI analytics application — text-to-SQL, LLM-assisted reporting, or AI-generated data narratives — and an understanding of where these tools succeed and where they introduce risk
AI standards and governance: developing or following prompt templates, output standards, or AI usage policies — with the ability to formalize these into a team-level standard

Nice To Haves

Databricks experience, including SQL warehouse and notebook-based analytics
Experience with dbt, Airflow, or equivalent orchestration and transformation tooling
Background in adtech, programmatic media, or performance marketing data environments
Experience contributing to or managing a reporting roadmap and formal request intake process
Snowflake warehouse optimization — clustering, materialized views, compute cost management
Familiarity with data governance standards, data contracts, or documentation frameworks
Experience coordinating with offshore or third-party analytics delivery teams
GenAI-to-warehouse integration experience — production text-to-SQL, LLM query layer, or agentic analytics at scale
People management experience or demonstrated team mentorship in a senior IC capacity

Responsibilities

Assess current analytics and reporting capabilities across tools, data sources, and consumer use cases. Identify gaps and recommend improvements to leadership, working within the direction set by the data engineering and data science teams.
Facilitate stakeholder discovery sessions to surface reporting requirements, prioritize requests, and ensure deliverables are tied to defined business objectives.
Document current-state data flows, reporting logic, and gap findings in a structured format for cross-functional review.
Contribute to BI architecture discussions and partner with data engineering on infrastructure decisions, semantic layer definition, and pipeline dependencies that affect reporting.
Administer and maintain the Apache Superset environment, including dashboard development, dataset management, user access, and first-line support.
Build and maintain SQL transformations and lightweight ELT pipelines to support analytical use cases and reporting data models.
Produce technical reference documentation covering ETL flows, data definitions, and pipeline monitoring so that dashboards and data products are maintainable without tribal knowledge.
Partner with stakeholders to scope, prioritize, and deliver dashboards and analytical reports aligned to business objectives.
Define and maintain an intake process for custom and ad hoc requests: requirements capture, role clarity, and published SLAs. Client-facing reporting is treated as a product experience output, not a pure ops function.
Manage a reporting backlog to enable structured iteration rather than continuous unplanned changes to live dashboards.
Apply GenAI tooling to analytics workflows, including text-to-SQL, LLM-assisted data exploration, and AI-accelerated reporting development against Snowflake, Databricks, and other core data sources.
Evaluate emerging AI-powered analytics tools and surface recommendations where they offer a clear, well-scoped business case. Maintain awareness of the text-to-SQL, agentic analytics, and LLM-augmented BI landscape.
Build and maintain the analytics team's Claude skill library — developing, documenting, and versioning prompt templates that meet Chalice's baseline standards for accuracy, output format, and analytical tone. Skills should cover recurring use cases: report summarization, data QA, stakeholder narrative generation, and SQL assistance.
Own an AI style guide for analytics outputs covering approved use cases, output formatting standards, accuracy validation requirements, and guardrails for client-facing AI-generated content. Ensure the team operates to a consistent and defensible standard when AI tooling is involved in delivered work.
Define norms for AI-assisted analytics work: when AI is appropriate, how outputs are validated before delivery, and how AI involvement is disclosed or documented internally.
As architecture and reporting templates mature, contribute to a plan for offshore or partner delivery to support analytics product development and maintenance at scale.
Support onboarding and skill development as the analytics team grows, contributing to documentation standards and internal knowledge transfer.