Principal Data Engineer

As a Principal Data Engineer at Self Financial, your primary responsibilities will include providing technical leadership for our data engineering operations, collaborating closely with product managers and data customers to translate strategic goals into actionable projects, and creating data models using large datasets. You'll oversee the management of data pipelines and ETL/ELT processes, ensuring our data remains a strategic asset. Additionally, you will guide your team in implementing best practices in data architecture and tool selection.

To be considered for the Principal Data Engineer role at Self Financial, candidates should have over 10 years of experience with database development and integration, as well as extensive expertise in complex Data Warehouse and Data Lake design. Proven leadership skills in a data engineering environment are essential, along with proficiency in managing large datasets with tools like AWS, SQL, and Apache Airflow. Strong communication and interpersonal skills are also vital to succeed in this position.

Collaboration within the Data Engineering Team at Self Financial is integral to our success. As a Principal Data Engineer, you will work side-by-side with diverse teams, such as Architecture, Infrastructure, and Product Management. This cross-functional approach allows you to implement impactful improvements efficiently. Regular brainstorming sessions and updates ensure every voice is heard, fostering an environment of innovation and shared goals.

At Self Financial, Principal Data Engineers primarily utilize SQL and Python to manage data pipelines and ETL/ELT processes. Experience with AWS data warehousing tools, including columnar data structures like Amazon RedShift, is crucial. Familiarity with software engineering tools such as Git, JIRA, and Apache Airflow is also beneficial for project management and team collaboration.

At Self Financial, we believe in fostering the growth of our employees, including Principal Data Engineers. You'll have the chance to lead projects that shape the company's direction and contribute to strategic decision-making. We also encourage continuous learning and provide educational assistance to pursue relevant training, certifications, and workshops. Working in a growth-centered environment, you can expect both personal and professional development opportunities.

When answering this question, discuss specific projects where you defined and implemented data architecture. Mention the tools and methodologies used, such as the Kimball approach, and highlight how your decisions impacted the overall data strategy. Use metrics or outcomes to showcase your success and effectiveness in ensuring data quality and accessibility.

In your response, explain your approach to managing large data sets during ETL processes, emphasizing techniques for optimizing performance and ensuring data integrity. Describe the tools you use, like Apache Airflow or SQL, and highlight any challenges you've faced and how you resolved them through creative problem-solving.

Discuss specific strategies you've employed for maintaining data quality, such as implementing validation rules or automated testing processes. Emphasize the importance of monitoring data pipelines, conducting regular audits, and collaborating with stakeholders to ensure data meets the required standards.

In your answer, talk about your methodology for prioritizing projects, perhaps by using frameworks like Agile or Kanban. Illustrate how you communicate with cross-functional teams to understand their needs, negotiate deadlines, and manage expectations while balancing project workloads.

When discussing your mentorship experience, share specific examples of how you've guided junior engineers: perhaps through code reviews, training sessions, or one-on-one coaching. Highlight the impact your mentorship had on their career progression and the success of the team as a whole.

Emphasize the role of metadata in creating a well-structured data environment. Discuss how it aids data discovery, management, and governance, as well as the impact on data quality and usability. Use examples from your experience where metadata has facilitated clearer communication and enhanced data strategy.

To effectively answer, describe your methods for staying informed about industry trends, such as following relevant blogs, attending conferences, or participating in professional forums. Emphasize a proactive approach to continuous learning and how you incorporate new tools or practices into your work.

Make sure to explain how automation enhances efficiency and consistency in your data engineering workflow. Share specific automation tools you've employed, such as automated testing or deployment processes, and discuss the benefits achieved in terms of time savings and error reduction.

In your response, define a clear plan for addressing data integrity issues, including identifying the root cause, assessing the impact, and implementing a solution. Discuss your communication strategy for keeping stakeholders informed, and the importance of learning from the incident to bolster future data governance.

When discussing your experience with database technologies, highlight your familiarity with specific systems like AWS Redshift, PostgreSQL, or others. Discuss any complex projects you've worked on with these technologies and how your expertise contributed to their success.