layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Director, Data Products & Analytics Strategy at solarwinds | LAYIQ

Define and maintain a cross-domain data products and analytics strategy spanning GTM, Finance, Customer, Product, Marketing, and other priority business areas, translating enterprise goals into a sequenced, outcome-focused roadmap
Work with senior business and functional leaders to frame business questions, translate ambiguous requirements into clear metric and product specifications, and align on practical measures of success
Design, build, and review production-grade dbt data models across staging, intermediate, and mart layers following SolarWinds modelling standards and creating consumption-ready, well-tested data products
Partner with domain delivery leads each quarter to jointly identify the highest-value, highest-complexity initiatives, and take a direct, hands-on build role on that critical subset — reinforcing delivery capacity rather than creating a parallel workstream
Use advanced SQL and Python for transformation, data quality, automation, exploratory analysis, custom dbt macros, and performance investigation — remaining hands-on enough to validate solutions in the codebase
Build or oversee high-quality Tableau dashboards and self-serve analytics assets from requirements through stakeholder sign-off, ensuring the experience is performant, understandable, and grounded in governed models and metrics
Drive alignment of cross-domain metrics, semantic definitions, lineage, and documentation with business owners and Governance, creating a trusted analytical language without taking ownership of source-system processes
Coordinate priorities, dependencies, and delivery standards across Analytics Engineering, Governance, and AI/ML, partnering with functional leaders to turn strategy into shipped products
Champion engineering and product discipline through dbt tests, CI/CD, Git workflows, observability, delivery SLAs, adoption measurement, and the retirement of redundant or low-value reporting
Work effectively with San Francisco / Bay Area-based leaders, communicate progress and trade-offs clearly, enable self-service analytics, and mentor practitioners across the distributed Enterprise Data team. Work with the team in-office approximately 1 day per week, generally on Wednesdays, but with some flexibility around frequency and day of week.
REQUIRED SKILLS & EXPERIENCE
7+ years of experience across analytics engineering, business intelligence, data products, analytics strategy, or related data roles, with experience leading cross-functional work and influencing senior stakeholders
Production-grade dbt experience (dbt Core or dbt Cloud) — you have designed and maintained multi-layer models at scale, written macros, implemented tests, and established reusable modelling patterns
Expert SQL skills — complex analytical queries, window functions, CTEs, aggregations, dimensional modelling, metric logic, and query performance awareness
Strong Python proficiency for analytics and data engineering use cases — data wrangling, automation, testing, custom tooling, exploratory analysis, or production workflows
Demonstrated ability to build governed, performant, and user-friendly Tableau dashboards or equivalent BI products from scratch and explain design decisions to executives and business users
Experience with a modern cloud data warehouse, preferably BigQuery, including materialisation, partitioning, clustering, cost, and performance trade-offs
Strong understanding of data modelling and analytics architecture — conformed entities, marts, star schema, slowly changing dimensions, semantic layers, metric normalisation, and lineage
Experience with Git, branch-based development, code review, and CI/CD in a data context — you apply software engineering discipline to analytics code
Proven ability to lead senior-stakeholder discovery and requirements conversations, communicate technical trade-offs clearly, and convert business needs into an actionable data-product brief
Experience setting priorities across multiple domains and delivery teams, resolving conflicting requirements, and building alignment without relying solely on formal authority
Practical experience using AI tools such as Claude, Gemini, GitHub Copilot, or similar to accelerate analysis, coding, documentation, and exploration. You apply sound engineering judgement to generated output: understanding and reviewing the code, validating results against requirements and source data, adding appropriate tests, and considering security, privacy, performance, and maintainability before anything reaches production
NICE TO HAVE
Hands-on MetricFlow or dbt Semantic Layer experience in a production environment
Experience in a SaaS or recurring-revenue business — ARR, bookings, pipeline, conversion, churn, renewals, expansion, and NRR metrics
Experience working with GTM, Finance, Customer, Product, and Marketing data, including Salesforce, NetSuite, Gong, Zoom, product-usage, or support data
Exposure to Dataplex, Fivetran, or similar data cataloguing, ELT, observability, and data-quality tooling
Experience rationalising reporting portfolios, increasing self-service adoption, and measuring the business value of data products
Experience working in a distributed, multi-location data organization and partnering with private-equity sponsors, board-facing teams, or other senior operating leaders

Director, Data Products & Analytics Strategy

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