Product Manager - Analytics

Product Manager

As a Product Manager at Ordway Labs, you will play a critical role in defining, developing, and managing all the analytics and reporting needs of Ordway billing and finance platform. You will collaborate with cross-functional teams to drive product innovation, ensure market fit, and deliver value to our customers. This role requires a strategic thinker with a strong understanding of product management principles and a passion for creating exceptional software solutions.

Job Responsibilities

Product Strategy & Roadmap

• Define and own the analytics product roadmap, aligning it with company data strategy and business priorities.
• Translate business intelligence needs into well-scoped, prioritized product requirements for dashboards, pipelines, and AI-powered analytics tools.
• Identify opportunities to leverage NLP, LLM-based agents, and intelligent automation to improve analytics workflows.
• Drive alignment across stakeholders — from data engineers and architects to executives and business unit leads.

Cross-Functional Collaboration

• Serve as the bridge between technical data teams and non-technical business stakeholders.
• Partner with Data Governance teams to ensure compliance, security, and data quality standards are embedded in product decisions.
• Support go-to-market efforts for analytics tools, including training, enablement, and change management.

Delivery & Execution

• Manage the analytics product backlog, running sprint planning, grooming, and retrospectives with data and engineering teams.
• Define KPIs and success metrics for all analytics features and track adoption, performance, and business impact post-launch.
• Oversee dashboard and visualization delivery, ensuring reports meet performance, usability, and governance standards.
• Champion documentation standards for data products, pipelines, and analytics architectures.

AI & Intelligent Analytics

• Own the product vision for NLP-based analytics interfaces, conversational BI agents, and LLM-powered data tools.
• Define use cases for AI/ML integration — including document understanding, anomaly detection, and intelligent automation in analytics workflows.
• Collaborate with engineers to evaluate and integrate LLM-based features into existing analytics products (e.g., natural language querying, AI-generated summaries).
• Set guardrails for AI-generated insights — defining quality thresholds, evaluation metrics, and user-facing explainability requirements.

Discovery & Requirements

• Conduct user research with business analysts, finance, operations, and leadership to surface unmet analytics needs.
• Author clear PRDs and user stories that data engineers, BI developers, and architects can act on immediately.
• Define acceptance criteria for SQL-driven reports, ETL pipeline outputs, and data warehouse deliverables.
• Partner with Analytics Architects to evaluate build-vs-buy decisions for data warehouse platforms (Snowflake, ClickHouse, Redshift) and visualization tools (Tableau, Power BI, Looker).

Qualifications

Preferred

• Python familiarity — able to read scripts and understand data transformation logic, even if not writing production code.
• Experience with real-time or streaming data products (Kafka, Spark) from a product ownership perspective.
• Familiarity with cloud data platforms (AWS, Azure, GCP) and associated managed analytics services.
• Experience with AI/ML-enabled product features — defining, launching, and evaluating models or LLM-based capabilities in production.
• Exposure to LLM applications (copilots, intelligent search, document understanding) particularly in finance or accounting contexts.
• Background in data governance, compliance, or data quality programs.
• Experience mentoring teams or running a center-of-excellence for analytics best practices.

Required

• 5+ years of product management experience, with at least 2–3 years focused on data, analytics, or BI products.
• Strong SQL literacy — able to read, interpret, and review complex queries; comfortable discussing query optimization and data modeling trade-offs.
• Hands-on familiarity with data warehouse platforms such as Snowflake, Redshift, or ClickHouse.
• Experience managing BI/visualization products — Tableau, Power BI, Looker, or similar.
• Working knowledge of ETL/ELT concepts and data pipeline architectures.
• Demonstrated ability to write PRDs and user stories for data-intensive, technically complex products.
• Experience defining metrics and success criteria for analytics features and data products.
• Strong facilitation and communication skills — ability to run discovery workshops, present roadmaps, and manage stakeholder expectations at all levels.