Engineering Lead, BI & Analytics

Swinerton•Concord, CA

2d•$160,000 - $190,000

About The Position

As the Engineering Lead, BI & Analytics, you will lead the design and delivery of trusted analytics products that help Swinerton move beyond static reporting toward faster, more proactive, AI-enabled decision-making. This hands-on engineering leadership role will advance BI, analytics, and data science capabilities through strong architecture, semantic models, dashboard standards, scalable analytical products, and emerging AI-enabled analytics patterns.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, Statistics, or a related field.
7+ years of experience across BI development, analytics engineering, data visualization, semantic modeling, BI architecture, data platforms, data science, applied analytics, or related technical roles, including experience leading technical teams or delivery efforts.
Strong expertise in SQL, data modeling, semantic modeling, metric definition, and modern BI and analytics development practices.
Practical fluency in data science concepts such as statistical analysis, predictive modeling, machine learning, experimentation, model evaluation, and responsible use of AI-enabled analytics.
Experience designing and delivering business-critical dashboards, reports, analytics products, predictive insights, and self-service reporting capabilities for enterprise audiences.
Experience with BI and analytics tools such as Power BI, Tableau, Looker, or equivalent technologies.
Experience with modern cloud data platforms and warehouse or lakehouse ecosystems such as Databricks, Snowflake, BigQuery, Azure data services, or equivalent technologies.
Experience establishing trusted analytics practices through governed metrics, data quality checks, validation, documentation, access controls, model evaluation, human oversight, and adoption support.
Experience defining dashboard standards, semantic model conventions, analytical product patterns, documentation practices, deployment approaches, and reusable delivery patterns for enterprise analytics solutions.
Experience translating business needs into clear requirements, analytics designs, data science problem framing, implementation plans, and well-scoped technical work.
Strong communication and stakeholder management skills, including the ability to explain tradeoffs, align cross-functional teams, and connect analytics, data science, and AI-enabled solutions to business value.

Nice To Haves

An advanced degree.
Power BI experience is strongly preferred.
Experience applying AI-enabled analytics patterns such as natural-language data exploration, anomaly detection, forecasting, recommendations, analytical copilots, or agentic analytics.
Construction industry experience; helpful but not required.

Responsibilities

Own the BI and analytics solution strategy by defining architecture, standards, and a roadmap for trusted reporting, semantic models, analytics products, data science solutions, and AI-enabled analytical experiences that support enterprise decision-making.
Identify opportunities to evolve analytics beyond traditional dashboards, including natural-language exploration, proactive insights, recommendations, anomaly detection, predictive analytics, and agentic analytics patterns.
Partner with business leaders, product owners, data engineering, platform teams, and AI stakeholders to translate business needs into scalable analytics and data science solutions aligned to measurable business outcomes.
Translate business priorities into solution approaches, dashboard requirements, semantic model needs, analytical methods, implementation plans, and well-scoped work.
Build scalable analytics products that combine data modeling, business context, intuitive user experience, reusable design patterns, and appropriate statistical or AI methods to enable trusted self-service, executive-ready insights, and proactive decision support.
Set standards for dashboard design, semantic modeling, metric definitions, report performance, analytical product design, documentation, testing, deployment, and reusable delivery patterns.
Guide the data science lifecycle from business problem framing and analytical approach through model development, validation, operationalization, and adoption, ensuring solutions are reliable, explainable, and connected to measurable business outcomes.
Lead, coach, and develop a team of BI developers and data scientists, supporting prioritization, delivery execution, issue resolution, technical growth, and cross-discipline collaboration.
Contribute directly to design reviews, code reviews, and resolution of complex engineering issues to help ensure solution quality, reliability, and production readiness.