Lead Data Engineer

As a Lead Data Engineer at Theoria Medical, you will define and drive key data engineering initiatives, architect scalable data solutions, and ensure robust governance practices. You'll collaborate with data scientists and analysts while leading projects from conception to implementation, all while mentoring junior engineers and optimizing system performance.

To become a Lead Data Engineer at Theoria Medical, you'll need a bachelor's degree in Computer Science or a related field and 5-7 years of experience in data engineering, including 2-3 years of experience in a leadership role. Understanding data warehousing, ETL/ELT processes, and data governance is essential, as is the ability to effectively communicate with both technical and non-technical stakeholders.

The work environment for a Lead Data Engineer at Theoria Medical is fully remote, allowing you the flexibility to work from anywhere. You’ll be part of a culture that values empowerment, accountability, and performance-based rewards, supported by a team that provides the essential tools necessary for your success.

As a Lead Data Engineer with Theoria Medical, you will work on a variety of projects that focus on developing robust data infrastructure, improving processing frameworks, and building scalable data pipelines. Each project will give you the opportunity to innovate and implement cutting-edge data technologies to enhance healthcare delivery.

Theoria Medical supports professional growth by fostering a culture of mentorship and encouraging continuous learning. As a Lead Data Engineer, you will not only lead your team but also have access to resources to enhance your skills, as well as opportunities to recommend and explore new data technologies.

Certainly! In my previous roles, I've designed and implemented data pipelines that ensure efficient data flow from various sources. My focus on scalability and maintenance aligns well with the expectations as a Lead Data Engineer, where I can leverage best practices and frameworks to optimize performance.

I believe in a hands-on approach to mentoring. This involves providing regular feedback, conducting knowledge-sharing sessions, and allowing them to take ownership of smaller projects to build confidence before tackling larger tasks. Leading by example is crucial in fostering a growth-oriented environment.

I prioritize establishing clear data governance frameworks and protocols, which include defining ownership, accountability, and data quality metrics. Regular audits and collaboration with stakeholders are key to maintaining high data standards.

I have hands-on experience with programming languages like Python and SQL, as well as ETL tools such as Apache NiFi and Talend. I'm also familiar with cloud platforms like AWS and Azure, which effectively support scalable data solutions.

One challenging project involved integrating disparate data sources into a unified data warehouse. I overcame obstacles by implementing Agile methodologies, which allowed for iterative testing and continuous stakeholder feedback, ultimately leading to a successful deployment.

I focus on breaking down technical topics into simple, relatable concepts and prioritize active listening. Finding common ground ensures that all stakeholders understand the objectives and implications of our data initiatives.

Currently, trends include the adoption of AI and machine learning in data analytics, increased focus on data privacy, and the use of serverless architectures for scalability. Staying updated with these trends is crucial for innovating solutions that meet business needs.

I regularly participate in webinars, industry conferences, and online courses. Networking with fellow professionals and engaging in community discussions on platforms like GitHub further keeps me informed about the latest developments.

Best practices for optimizing performance include monitoring system metrics, performing regular performance tuning of queries, and utilizing caching strategies. Prioritizing efficient schema designs also plays a vital role in ensuring smooth data processing.

In my previous roles, I have led multiple data engineering projects, from initial requirement gathering to final implementation. I employ Agile practices, set clear goals, and foster team collaboration to ensure each phase is aligned with overall organizational objectives.