Senior Data Engineer

As a Senior Data Engineer at Huda Beauty, you'll be responsible for leading the design and implementation of comprehensive data solutions. This includes creating scalable data ecosystems on platforms like GCP, Azure, and Databricks, as well as developing machine learning pipelines and optimizing real-time data processing using streaming technologies. Your role will also involve implementing data governance frameworks and mentoring junior engineers, fostering a collaborative environment.

To apply for the Senior Data Engineer role at Huda Beauty, candidates should possess a Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, alongside 8+ years of professional experience in data engineering. Proven expertise in cloud platforms like GCP, Azure, and Databricks, along with strong programming skills in languages such as Python or Java, are essential for this position.

As a Senior Data Engineer at Huda Beauty, familiarity with cloud data platforms such as GCP, Azure, and Databricks is crucial. Additionally, you should be experienced in data processing frameworks like Spark, Hive, and Hadoop, as well as real-time streaming technologies such as Kafka and Apache Beam. Knowledge of data security tools and workflow orchestrators is also highly beneficial.

Huda Beauty is committed to the growth and development of its team members. For Senior Data Engineers, we offer various perks including monthly coaching sessions with our in-house Success Coach and opportunities to mentor junior engineers. We aim to foster a collaborative culture where continuous learning and professional advancement are prioritized.

Senior Data Engineers at Huda Beauty enjoy a hybrid work environment, which offers a balance of remote and in-office work. This flexibility allows you to create a work schedule that suits your lifestyle while collaborating with a dynamic team of professionals dedicated to innovation and excellence in the beauty industry.

When answering this question, highlight specific projects you've worked on involving cloud platforms like GCP or Azure. Detail the architecture you implemented, the challenges faced, and how you optimized performance or scalability. Mention any relevant certifications that demonstrate your proficiency.

Discuss your understanding of data governance frameworks and the steps you've implemented to ensure data security. Highlight your familiarity with tools like Hashicorp Vault or governance protocols. Provide examples of how you've ensured compliance with data privacy regulations in previous roles.

Share specific techniques or tools you've employed for optimizing real-time data processing, such as Apache Beam or Spark Streaming. Discuss the architecture you used, the metrics you monitored, and any improvements in performance or efficiency you achieved.

Provide a concrete example of a machine learning project where you took the lead. Discuss the data pipeline you designed, how you integrated the model into production, and the outcome based on performance metrics. Highlight your collaboration with cross-functional teams.

Explain your project management approach, focusing on how you prioritize tasks based on project scope and deadlines. Mention any tools you use for tracking progress and collaboration, and highlight your communication strategy with team members and stakeholders.

Discuss best practices including maintaining clear documentation, ensuring data quality and consistency, and building modular and reusable components. Provide insights into your testing process and how you monitor pipeline performance over time.

Share your approach to mentoring and how you've helped junior engineers develop their skills. Discuss any specific programs or initiatives you've led to promote their growth and how mentoring benefits both the individual and team dynamics.

Choose a technical concept, such as data partitioning or an algorithm, and break it down into simpler terms. Use analogies or real-world examples to illustrate your explanation, showcasing your communication skills, which is vital for collaborative teams.

Emphasize the importance of working closely with data scientists, analysts, and other engineers to develop effective data solutions. Share examples of successful collaborative projects and how cross-functional teamwork has enhanced outcomes.

Discuss your methods for keeping up-to-date, such as attending webinars, participating in online forums, or following industry leaders. Highlight any relevant courses or certifications you've pursued to continually improve your skills.