Lead Data Engineer

As a Lead Data Engineer at BJSS in Columbus, you'll be responsible for leading a team of data engineers to deliver high-quality data integration solutions. This includes designing and developing robust data pipelines, ensuring data quality, and collaborating with stakeholders to meet data requirements. You'll also optimize workflows for performance and mentor junior team members to foster continuous learning.

Candidates for the Lead Data Engineer position at BJSS should have extensive experience in data engineering, particularly in data integration and analytics. Proficiency in Microsoft technologies like Azure Data Factory, SQL Server, and Azure Databricks is essential. Strong problem-solving skills and the ability to work collaboratively in a team environment are also important.

The Lead Data Engineer plays a crucial role in supporting business decision-making at BJSS by collaborating closely with the analytics team. You'll ensure that data is integrated effectively and is accurate, which allows for data-driven insights that drive strategic planning and operational efficacy within the organization.

In the Lead Data Engineer position at BJSS, you will work extensively with Microsoft technologies such as Azure Data Factory, SQL Server, Azure Databricks, and Power BI. You'll also utilize programming languages like SQL and Python to develop and maintain data integration solutions tailored to the needs of stakeholders.

At BJSS in Columbus, the work environment is collaborative and supportive, with a focus on continuous learning and innovation. You'll have the opportunity to work in a team-driven atmosphere while enjoying flexibility in your work-life balance. Our local offices foster tight-knit communities that enhance teamwork and engagement.

When addressing your experience with data integration and analytics, be sure to highlight specific projects where you led data engineering solutions. Include challenges faced and how you overcame them, particularly using Microsoft technologies and your collaborative efforts with cross-functional teams to meet stakeholder needs.

In your response, discuss the best practices you implement to guarantee data quality, such as validation checks, automated testing, and maintaining data integrity throughout the ETL processes. Mention any specific tools or methodologies you utilize to uphold standards in your data pipelines.

When discussing your leadership and mentoring strategies, focus on how you foster a culture of learning and support within your team. Mention your approach to providing constructive feedback, setting objectives, and encouraging continuous professional development through training opportunities.

Share specific situations where you successfully improved the performance of a data pipeline. Focus on the techniques used, such as adjusting ETL processes, enhancing SQL queries, or implementing caching strategies, and the results of your optimizations in terms of efficiency gains or reduced costs.

In your answer, mention the importance of continuous learning and professional development. Highlight resources like industry publications, online courses, and attendance at conferences or webinars that you utilize to stay updated on the latest trends and innovations in data engineering.

Explain how you prioritize strong communication and relationship-building with analytics teams. Discuss how you ensure data engineers and analysts align on project goals, share insights, and work together to create solutions that support data-driven decision-making.

Use this question to showcase your problem-solving skills. Detail a specific challenging project, the obstacles faced, and the solutions you implemented while involving your team. Highlight the lessons learned and how these experiences have shaped your approach to future data challenges.

Discuss your experience with cloud platforms, particularly Microsoft Azure. Detail how you've utilized its services to create scalable data solutions, manage resources effectively, and ensure data security across cloud integrations, emphasizing the benefits these technologies bring to data engineering.

Mention your proficiency in SQL, Python, and any other relevant languages. Discuss specific applications of these languages in data modeling, ETL processes, or data transformation tasks, highlighting projects where your coding expertise made a significant impact.

When answering this question, provide examples of previous experiences where you successfully managed tight deadlines. Highlight your techniques for prioritizing tasks, efficient time management, and maintaining team morale to ensure project completion while ensuring quality.