Lead Data Engineer

As a Lead Data Engineer at Shift4, you will be responsible for designing and managing our data architecture, leading a team of data engineers, and developing robust ETL processes. You will also oversee data quality standards, collaborate with analytics teams to support the creation of BI tools, and manage data-related projects from conception to execution. Your leadership will help ensure that data handling is efficient, secure, and aligned with business goals.

To be considered for the Lead Data Engineer position at Shift4, you should have a minimum of 7 years of experience in data engineering or a similar field, with at least 1 year in a leadership role. Proficiency in SQL and big data technologies, along with strong coding skills in programming languages like Python or Java, are essential. Familiarity with cloud services and data warehousing solutions is also required, alongside strong analytical and communication skills.

A Lead Data Engineer at Shift4 will work on a variety of projects aimed at enhancing our data architecture and processes. This includes developing data pipelines, ensuring data quality, and collaborating on analytics projects. You will lead initiatives to implement new technologies and improve the efficiency of data management, making your role critical in driving successful business and data strategies.

Yes, mentorship is a key aspect of the Lead Data Engineer role at Shift4. You will be responsible for leading and mentoring junior data engineers, fostering a culture of continuous learning and improvement within the team. Your guidance will be instrumental in helping your team members develop their skills and grow in their roles.

At Shift4, we believe in the importance of continuous professional development. As a Lead Data Engineer, you will have opportunities to attend industry conferences, participate in training sessions, and earn certifications in relevant technologies. We also encourage you to explore new tools and innovations that can benefit our data engineering practices, supporting your career growth in a dynamic environment.

When designing a scalable data architecture, I focus on understanding the data needs of the business and identifying key data sources. I prioritize flexibility and security, utilizing cloud solutions for scalability and ensuring redundancy. Utilizing best practices in data modelling and pipeline orchestration tools helps in achieving efficient and manageable data flow.

My experience with ETL processes involves developing robust ingestion frameworks that transform raw data into usable formats. I've used various tools to build efficient ETL pipelines that ensure data integrity and speed, and I continuously monitor and optimize these processes for better performance.

I am proficient in big data technologies such as Hadoop and Spark. These platforms have allowed me to handle large datasets and perform complex processing efficiently. My experience includes leveraging these tools for data processing tasks and integrating them with cloud services for optimized storage solutions.

Ensuring data quality starts with implementing validation checks during the data ingestion phase and establishing clear data governance policies. I regularly conduct audits and use automated testing to monitor data accuracy and integrity, making adjustments when discrepancies arise.

I have substantial experience using cloud services such as AWS, Azure, and Google Cloud for deploying data solutions. These platforms support rapid scalability and flexibility and have enabled me to build robust data lakes and warehouses that facilitate easy access for analytics and reporting.

As a Lead Data Engineer, I would mentor my team by providing regular feedback, sharing best practices, and facilitating learning opportunities. I believe in creating an open environment where team members feel comfortable asking questions and exploring new concepts, which fosters growth and collaboration.

My process for optimizing data pipelines involves analyzing performance metrics and identifying bottlenecks during data flow. I utilize efficient coding techniques, ensure proper indexing, and implement parallel processing where applicable. Continuous monitoring allows me to proactively address issues and maintain optimal performance.

In a previous role, I managed a project that involved integrating multiple data sources into a unified data warehouse. The project included a tight deadline and required cross-functional collaboration. I utilized agile management techniques to ensure milestones were met and regularly communicated updates to stakeholders, which led to successful project completion.

To stay updated, I regularly attend industry conferences, participate in online courses, and engage with community forums. Following thought leaders on platforms such as LinkedIn and Medium helps keep my knowledge current, along with experimenting with new tools to enhance my skill set.

While I focus mainly on data engineering, I have a foundational understanding of machine learning pipelines and feature engineering. I've collaborated with data scientists to ensure data availability and quality for machine learning models, bridging the gap between data engineering and analytics.