Data Analytics Engineer 2

The Role: Data Analytics Engineer II

The Data Analytics Engineer II develops engineering, statistical, and data solutions that support the performance and lifecycle management of critical infrastructure across Switch data centers. This role works with data related to power, water, cooling, and other infrastructure systems to support utility resourcing, capacity management, efficiency, root cause analysis, design feedback, condition-based maintenance, end-of-life planning, and continuous improvement.

You will build and maintain data pipelines, analytical models, warehouses, applications, and automation that turn infrastructure data into reliable information for engineering and operational decisions. You will also support statistical and machine-learning workflows and help integrate emerging AI capabilities into practical engineering and business applications.

This role partners closely with Operations, Commissioning, Construction, Engineering, and other stakeholders to ensure data solutions address real operational needs and support reliable data center performance.

What You’ll Do

• Build and operate end-to-end data systems, including acquiring data from APIs, databases, and external systems.
• Design data models and develop scalable ETL/ELT pipelines that load, cleanse, transform, and unify data for analytics.
• Design, optimize, and maintain analytical data warehouses and supporting schemas.
• Develop high-performance Starburst SQL queries, views, and transformations to support large-scale reporting and analysis.
• Build and maintain data-driven web applications that connect backend APIs with analytical databases.
• Partner with application development teams to deploy data applications securely and reliably at scale.
• Support machine-learning and statistical workflows by developing automated feature-generation pipelines and providing clean, accessible datasets.
• Develop automation that reduces repetitive data-processing work and improves consistency.
• Design and deploy generative AI applications and autonomous agents using large language models, APIs, and orchestration frameworks.
• Apply AI capabilities to automate workflows, improve user experiences, and support scalable engineering and operational solutions.
• Monitor and optimize data pipelines, databases, applications, and supporting systems for reliability, performance, and scalability.
• Establish and maintain data-quality standards and controls.
• Implement appropriate data security and compliance measures to protect sensitive information.
• Develop engineering and statistical models that support analysis of data center power, water, cooling, capacity, availability, efficiency, and lifecycle performance.
• Analyze complex datasets and translate findings into practical recommendations for engineering, operations, and leadership.
• Support root cause analysis, continuous improvement, condition-based maintenance, capacity management, and infrastructure lifecycle programs.
• Provide data and performance insights that inform design feedback and infrastructure improvement decisions.
• Partner with Operations, Commissioning, Construction, Engineering, and other stakeholders to understand requirements and develop appropriate data solutions.
• Identify opportunities to improve analytical methods, tools, automation, and data workflows.

What We’re Looking For

• You use data and engineering knowledge together to solve practical infrastructure problems.
• You can work across data engineering, analytics, software, and engineering domains rather than treating them as separate disciplines.
• You analyze complex datasets and translate findings into clear recommendations.
• You understand how data quality, architecture, and system reliability affect the decisions people make from the data.
• You identify problems, evaluate available information, and develop practical solutions.
• You communicate technical findings clearly to engineering, operational, and business stakeholders.
• You work effectively across Operations, Commissioning, Construction, and Engineering teams.
• You remain flexible, proactive, resourceful, and efficient when requirements or priorities change.
• You work effectively under pressure and maintain focus on reliable outcomes.
• You share technical knowledge and contribute to the development of the broader team.

What You’ll Bring

Required Qualifications

• Bachelor of Science or higher in Mechanical Engineering, Electrical Engineering, Computer Science, or an equivalent math-intensive discipline.
• 2+ years of experience supporting critical infrastructure or data center operations.
• 1+ years of experience in an engineering role.
• 1+ years of experience in a data science, data analytics, or comparable analytical role.
• Advanced understanding of electrical and mechanical systems used in data center environments, which may include electrical feeders, transformers, generators, switchgear, UPS systems, power systems, ATS/STS units, PDU units, air handling units, cooling towers, pumping systems, and fire suppression systems.
• Ability to interpret P&IDs, wiring diagrams, schematics, sequences of operation, submittals, and MEP drawing packages.
• Strong understanding of data analysis methodologies and statistical techniques.
• Experience with database management and data analysis tools.
• Experience using Python or another programming or cloud-computing language.
• Experience working with SQL and analytical data environments.
• Ability to analyze complex information and develop solutions to technical problems.
• Ability to translate analytical findings into recommendations for stakeholders and leadership.
• Strong Microsoft Outlook, Publisher, Word, Excel, and PowerPoint skills.

Preferred Qualifications

• Professional Engineer (PE) license.
• Experience developing scalable ETL/ELT pipelines and analytical data models.
• Experience with Starburst or comparable distributed SQL/query platforms.
• Experience developing data-driven web applications and API integrations.
• Experience supporting machine-learning or statistical modeling workflows.
• Experience developing generative AI applications, LLM-based workflows, or autonomous agents.
• Experience supporting infrastructure capacity, reliability, efficiency, condition-based maintenance, or lifecycle programs.

We recognize that qualified candidates may not meet every preferred qualification. If your experience aligns with the core responsibilities of the role, we encourage you to apply.