Senior Director, Reporting & Analytics Engineering

• Bachelor's degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, Economics, or a related quantitative field; equivalent professional experience will be considered.
• 15+ years of progressive experience in data, analytics, or business intelligence, including 8+ years leading teams and 4+ years managing managers.
• Demonstrated experience leading an organization of 25 or more people in a large, matrixed enterprise.
• Strong hands-on foundation in SQL, dimensional modeling, and ELT/ETL design, with production experience on a modern cloud data warehouse or lakehouse (for example Snowflake, Databricks, BigQuery, Redshift, or Synapse) and a transformation framework such as dbt.
• Experience applying software engineering practices to analytics work, including Git-based workflows, automated testing, and CI/CD.
• Enterprise-scale leveraging of BI and data visualization platforms such as Domo, Tableau, Power BI, or Looker, including governance, provisioning, adoption, and cost management.
• Demonstrated ability to participate in driving data governance program covering catalog, lineage, glossary, stewardship, quality, and access, using platforms such as Atlan, Collibra, Alation, or Informatica.
• Proven track record of delivering jointly with Data Engineering and Product Management, with clear ownership boundaries and shared accountability.
• Direct experience partnering with Data Science, including operationalizing, visualizing, and monitoring model output for business consumption.
• Working command of the statistical concepts underlying exception detection, including hypothesis testing, confidence intervals, variance and control limits, seasonality, and forecasting.
• Experience running a formal intake, prioritization, and capacity planning process for a shared services or platform organization.
• Excellent executive communication and influence skills, with the ability to operate effectively amid ambiguity and competing priorities.
• Experience managing budget, vendor contracts, and platform licensing.

• Master's degree or MBA in a related field.
• Production experience delivering LLM-based analytics: retrieval-augmented generation over governed data, text-to-SQL or conversational BI, semantic layers designed for model consumption, and prompt, evaluation, and cost monitoring frameworks.
• Experience designing agentic workflows and orchestration for monitoring, investigation, and notification use cases.
• Hands-on experience with Atlan, Domo, and Tableau specifically.
• Proficiency in Python or R for prototyping, and familiarity with common statistical and ML libraries and MLOps concepts.
• Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management.
• Familiarity with data product and data mesh operating models, domain-aligned ownership, and data contracts.
• Experience operating in an Agile or product operating model at scale, including portfolio, OKR, and roadmap management. Deep experience with the “Silicon Valley” school of Product Management, as exemplified by the “Marty Kagan”-style academic approach a significant plus.
• Experience leading global or matrixed teams, including offshore or vendor-partnered delivery models.
• Track record leading a major platform migration, consolidation, or reporting rationalization effort.
• Demonstrated success attracting, developing, and retaining senior technical talent in a competitive market.

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• Lead, coach, and develop an organization of approximately 30 people, including managing through frontline managers; own hiring, role clarity, career pathing, performance management, and succession planning.
• Define a multi-year vision and roadmap for enterprise reporting and analytics engineering, and translate it into quarterly outcomes the team and its partners can measure. Partner closely with Product Management to jointly shape the multi-year enterprise end-to-end data strategy.
• Own the organization's budget, vendor relationships, and contractor or offshore capacity.
• Establish and sustain an engineering culture within an analytics function: peer review, automated testing, documentation standards, source control, CI/CD, etc.

• Drive maturity of the last-mile architecture in partnership with Data Engineering: curated marts, semantic layers, reusable data products, and certified datasets that serve reporting, ai enablement, data science, and downstream applications.
• Define and enforce dimensional modeling standards, transformation frameworks, and modular, tested, version-controlled analytics code.
• Own the enterprise metric layer so that key business measures carry a single, governed definition regardless of where they are consumed.
• Partner with Data Engineering to define clear contracts and handoffs between pipeline and platform work and last-mile modeling, including shared tooling, standards, and escalation paths.
• Manage query performance, warehouse consumption, and platform cost as first-class engineering concerns.

• Own cataloging, business glossary, data certification, and stewardship workflows, leveraging tools such as Atlan as the primary metadata platform.
• Maintain column-level lineage across the analytics estate to support impact analysis, change management, audit, and root-cause investigation.
• Leverage data access provisioning and entitlement models, including row- and column-level security, in partnership with Security, Privacy, and Compliance.
• Collaborate with Data Engineering to drive data quality monitoring, freshness and availability SLAs, observability, and incident response for analytic assets.

• Drive analytics engagement by leveraging enterprise visualization and provisioning platforms, including Domo and Tableau, in partnership with Data Engineering. Ensure best practices are followed in data architecture, governance, adoption, performance, and total cost management.
• Set visualization and information design standards that make reports readable, consistent, accessible, and decision-oriented.
• Rationalize the existing reporting portfolio: retire redundant and unused assets, consolidate overlapping content, drive down tech debt and drive consumption toward certified sources.
• Build a durable self-service capability through training, templates, community, office hours, and clear guardrails on what belongs in self-service versus centrally managed content.

• Partner with Data Science to productize their work: build the visualization, interaction, monitoring, and feedback loops that turn models and research into products the business actually uses.
• Partner with Product Management and Data Engineering to enable generative AI and LLM access to enterprise data. This includes preparing the semantic and metadata foundation that makes data legible to models, delivering conversational analytics, text-to-SQL, and retrieval-augmented experiences on governed sources, and establishing guardrails, evaluation, and accuracy monitoring so that answers can be trusted.
• Build exception-based analytics that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds.
• Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune sensitivity and volume to prevent alert fatigue and preserve signal.
• Design and deploy analytic agents that proactively monitor data, investigate variances, assemble narrative explanations, and deliver insight into the tools where people already work.
• Shift the organization's consumption model from pull to push: the measure of success is not how many people opened a dashboard, but whether the right person was told the right thing at the right time.

• Operate as a true partner to Product Management: contribute to roadmap, requirements, user research, and release planning, and run analytics with a product mindset covering personas, adoption metrics, and asset lifecycle management.
• Partner with Analytics Operations to run a transparent intake, triage, sizing, and prioritization process, with published capacity, explicit tradeoffs, and reliable delivery commitments.
• Make capacity constraints visible to stakeholders and executives, drive reuse over one-off builds, and decline or defer work with evidence rather than friction.
• Serve as a senior, executive-facing partner to business functions, translating ambiguous business questions into well-scoped analytic solutions.