Senior Director, Reporting & Analytics Engineering

Description

The company makes hardware and network services used by more than 100 million people, and it is also a major entertainment producer with well-known intellectual property. It operates as a subsidiary of a larger parent group.

The role leads the enterprise reporting and analytics engineering group, which has analytics engineers, report developers, visualization specialists, and people managers. The group owns the final stage of the enterprise data supply chain: semantic models, curated data products, metric definitions, and the tools people use to turn data into decisions.

Traditional dashboards and filters are becoming less central, but the demand for timely, clear, actionable insight is not going away. Governed dashboards and trusted reporting stay the base, and this leader must run that base well. The larger goal is to move the group from static reports to proactive, intelligent analytics, including productizing data science models, enabling generative AI and LLM access to company data, alerting users when outcomes move outside expected statistical ranges, and running agents that watch data and surface insight unprompted.

Analytics engineering uses many of the same methods as data engineering, such as modeling, transformation, testing, version control, CI/CD, and attention to performance and cost, but it focuses on business use rather than platforms and pipelines. Success depends on strong partnerships with Data Engineering at the platform and consumption boundary, Product Management on roadmap and requirements, Data Science on advanced analytic products, and Analytics Operations on intake and prioritization that uses limited capacity well.

The ideal candidate has years of experience with governance, metadata, lineage, dimensional modeling, and enterprise BI platforms, and wants to apply that discipline to a new generation of analytic products.

Responsibilities

• Lead, coach, and grow a team of about 30 people, including managers who lead frontline staff. Own hiring, role clarity, career paths, performance management, and succession planning.
• Set a multi-year vision and roadmap for enterprise reporting and analytics engineering, and break it into quarterly outcomes the team and its partners can measure. Work closely with Product Management to shape a multi-year enterprise end-to-end data strategy.
• Own the group's budget, vendor relationships, and contractor or offshore capacity.
• Build and maintain an engineering culture in an analytics group, with peer review, automated testing, documentation standards, source control, and CI/CD.
• Drive the last-mile architecture forward with Data Engineering, including curated marts, semantic layers, reusable data products, and certified datasets for reporting, AI, 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 each key business measure has one governed definition wherever it is used.
• Work with Data Engineering to set clear contracts and handoffs between pipeline work and last-mile modeling, covering shared tooling, standards, and escalation paths.
• Treat query performance, warehouse consumption, and platform cost as core engineering concerns.
• Own the data catalog, business glossary, data certification, and stewardship workflows, using Atlan as the main metadata platform.
• Maintain column-level lineage across the analytics estate to support impact analysis, change management, audit, and root-cause work.
• Use data access provisioning and entitlement models, including row- and column-level security, together with Security, Privacy, and Compliance.
• With Data Engineering, drive data quality monitoring, freshness and availability SLAs, observability, and incident response for analytic assets.
• Drive adoption of enterprise visualization and provisioning platforms, including Domo and Tableau, together with Data Engineering. Make sure best practices cover data architecture, governance, adoption, performance, and total cost.
• Set visualization and information design standards so reports are readable, consistent, accessible, and built for decisions.
• Rationalize the reporting portfolio by retiring redundant and unused assets, merging overlapping content, reducing tech debt, and moving consumption to certified sources.
• Build a lasting self-service capability through training, templates, community, office hours, and clear rules on what belongs in self-service versus central management.
• Work with Data Science to productize their models, building the visualizations, interactions, monitoring, and feedback loops that make the output useful to the business.
• Work with Product Management and Data Engineering to enable generative AI and LLM access to enterprise data. Prepare the semantic and metadata foundation that lets models interpret data, deliver conversational analytics, text-to-SQL, and retrieval-augmented experiences on governed sources, and set up guardrails, evaluation, and accuracy monitoring.
• Build exception-based analytics that flag when an outcome departs from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection instead of fixed thresholds.
• Send exceptions to accountable owners with context, likely causes, and a recommended next step. Tune sensitivity and volume to limit alert fatigue and keep the signal strong.
• Design and deploy analytic agents that monitor data, investigate variances, write narrative explanations, and deliver insight in the tools people already use.
• Move the organization from pull to push consumption. Success means the right person hears the right thing at the right time, not how many people opened a dashboard.
• Act as a real partner to Product Management: contribute to roadmap, requirements, user research, and release planning, and manage analytics with a product mindset that covers personas, adoption metrics, and asset lifecycle.
• With Analytics Operations, run a transparent process for intake, triage, sizing, and prioritization, with published capacity, clear tradeoffs, and dependable delivery commitments.
• Make capacity limits visible to stakeholders and executives, favor reuse over one-off builds, and decline or defer work using evidence.
• Serve as a senior, executive-facing partner to business functions, turning unclear business questions into well-defined analytic solutions.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, Economics, or a related quantitative field. Equivalent professional experience will also be considered.
• 15+ years of steadily growing experience in data, analytics, or business intelligence, including 8+ years leading teams and 4+ years managing managers.
• Proven experience leading an organization of 25 or more people in a large, matrixed enterprise.
• Strong hands-on skill in SQL, dimensional modeling, and ELT/ETL design, with production work on a modern cloud warehouse or lakehouse such as Snowflake, Databricks, BigQuery, Redshift, or Synapse, plus a transformation framework such as dbt.
• Experience applying software engineering practices to analytics, including Git-based workflows, automated testing, and CI/CD.
• Enterprise-scale experience with BI and visualization platforms such as Domo, Tableau, Power BI, or Looker, covering governance, provisioning, adoption, and cost management.
• Ability to help drive a data governance program covering catalog, lineage, glossary, stewardship, quality, and access, using platforms such as Atlan, Collibra, Alation, or Informatica.
• Track record of delivering work with Data Engineering and Product Management, with clear ownership boundaries and shared accountability.
• Direct experience working with Data Science, including putting model output into production, visualizing it, and monitoring it for business use.
• Working command of the statistics behind 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, and the ability to work effectively amid ambiguity and competing priorities.
• Experience managing budgets, vendor contracts, and platform licensing.
• Preferred: Master's degree or MBA in a related field.
• Preferred: Production experience with LLM-based analytics, including retrieval-augmented generation over governed data, text-to-SQL or conversational BI, semantic layers built for model use, and frameworks for prompts, evaluation, and cost monitoring.
• Preferred: Experience designing agentic workflows and orchestration for monitoring, investigation, and notification.
• Preferred: Hands-on experience with Atlan, Domo, and Tableau specifically.
• Preferred: Proficiency in Python or R for prototyping, plus familiarity with common statistical and ML libraries and MLOps concepts.
• Preferred: Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management.
• Preferred: Familiarity with data product and data mesh operating models, domain-aligned ownership, and data contracts.
• Preferred: Experience working in an Agile or product operating model at scale, including portfolio, OKR, and roadmap management. Deep familiarity with a Silicon Valley style of product management in the Marty Kagan tradition is a significant advantage.
• Preferred: Experience leading global or matrixed teams, including offshore or vendor-partnered delivery.
• Preferred: Track record leading a major platform migration, consolidation, or reporting rationalization effort.
• Preferred: Proven success attracting, developing, and keeping senior technical talent in a competitive market.

Conditions

Background checks are run at the offer stage for all new hires, which may include criminal background checks for some roles, and this requires processing personal information.