Senior Solutions Data Architect - Remote Job

As a Senior Solutions Data Architect at Bertoni Solutions, you will be responsible for translating complex project requirements into actionable strategies, designing data systems, and collaborating with diverse teams to ensure project success. Your role includes engaging directly with clients, managing project timelines, and creating analytical tools to improve healthcare outcomes.

To qualify for the Senior Solutions Data Architect role at Bertoni Solutions, candidates must hold a Bachelor’s degree in a related field, possess 3-5+ years of relevant experience, and have expertise in modern data technologies like Snowflake and Azure. Strong communication skills in English and familiarity with healthcare data are also essential.

A Senior Solutions Data Architect at Bertoni Solutions should be proficient in modern data and cloud technologies, including Snowflake, SQL Server, Tableau, Power BI, and Azure. Experience with creating data models and knowledge of statistics and machine learning is highly beneficial.

Yes, the Senior Solutions Data Architect position at Bertoni Solutions is a fully remote opportunity. However, candidates must be located in Latin America, specifically Lima, Peru, to qualify for this role.

The interview process for the Senior Solutions Data Architect at Bertoni Solutions consists of several stages, including an initial interview with the company, a follow-up interview with the partner, and two interviews with the final client. This thorough approach ensures alignment between candidates and project needs.

When gathering requirements for a data architecture project, I prioritize understanding both the technical and business perspectives. I engage stakeholders through meetings and workshops, using open-ended questions to uncover their needs and challenges. Documenting requirements clearly ensures alignment and guides our development efforts.

I have extensive experience in data modeling, where I've designed and implemented custom models for performance analytics and operational reporting. I focus on understanding the data flow and ensuring that the models support accurate decisions while reflecting the business requirements of our clients.

To manage multiple complex projects, I prioritize effective project management techniques such as creating detailed timelines, assigning roles, and using collaborative tools for real-time updates. Regular check-ins with team members also help address any challenges early on, ensuring we meet client expectations.

In my previous roles, I have worked extensively with cloud technologies like Azure and Snowflake, using them to build scalable data architectures and analytics solutions. My experience includes managing cloud migrations and optimizing performance through best practices in data storage and access.

I ensure effective communication with clients by setting clear expectations from the start and providing regular updates through meetings and reports. Actively seeking feedback also fosters collaboration and trust, ensuring that any changes or developments align with their needs.

My approach to mentoring junior team members involves active involvement and encouragement. I provide them with opportunities to engage in projects, share knowledge through training sessions, and offer constructive feedback to build their confidence and skills.

When project scope or requirements change, I handle it by closely collaborating with stakeholders to assess the impact. I work to realign project timelines and deliverables while keeping the lines of communication open, ensuring everyone understands the changes and their implications.

To stay updated on advancements in data technologies, I regularly participate in webinars, read articles and whitepapers, and engage with professional communities. Networking with peers in the industry also allows me to share insights and learn about practical applications of new technologies.

In a past project, I encountered a significant challenge with data integration from multiple sources. I conducted a thorough analysis and designed a robust ETL process that enabled seamless data flow, resulting in improved reporting accuracy and efficiency, ultimately satisfying the client’s needs.

Evaluating the success of a data architecture solution involves assessing key metrics such as performance, user satisfaction, and analytical capabilities. I've established KPIs, collected user feedback, and monitored system performance to measure success and identify areas for improvement.