Manager, Data Engineering

The Manager, Data Engineering at AAA Life Insurance Co. is responsible for driving the vision for data platforms and executing plans that align with that vision. Key responsibilities include collaborating with stakeholders, delivering new data sources, developing data pipelines, and optimizing internal processes.

To qualify for the Manager, Data Engineering role at AAA Life Insurance Co., candidates should hold a Master’s degree in Information Technology, Analytics, or a related field, possess AWS Professional or Specialty Certification, and have at least two years of relevant experience in data analytics or software development.

Candidates for the Manager, Data Engineering position at AAA Life Insurance Co. should be proficient in Python, SQL, and AWS big data technologies such as Redshift, Qlikview, Tableau, Hive, and Spark-Streaming to effectively manage data products and pipelines.

Yes, AAA Life Insurance Co. does allow for telecommuting three times a week for the Manager, Data Engineering role, promoting a flexible work environment while maintaining collaboration with the team.

The Manager, Data Engineering will significantly impact AAA Life Insurance Co. by enhancing analytical decision-making, improving customer experiences, and building a powerful data infrastructure that supports business growth and innovation.

I have extensive experience using AWS big data technologies including Redshift and Spark-Streaming. I worked on projects where I implemented scalable data solutions that improved data processing speed and accuracy, which is vital for decision-making.

I start by collaborating with stakeholders to identify their data needs, then I design the pipelines to ensure they efficiently extract, transform, and load data. Automation and optimization are key focuses in my approach.

I implement comprehensive data validation checks at various stages of the pipeline and utilize monitoring tools to track data quality over time. Regular audits and feedback loops are also essential.

I foster open communication and encourage collaboration among team members. Regular check-ins and team-building activities help build trust and ensure everyone is aligned on project goals.

One major challenge was scaling our data infrastructure as user demand grew. I addressed this by analyzing performance metrics and redesigning our data architecture to improve efficiency and scalability.

I invest time in continued learning through online courses, webinars, and industry conferences. Networking with other professionals also helps me stay informed about emerging trends and technologies.

Stakeholder collaboration is crucial. It allows me to understand their needs and expectations, ensuring the data products we build align with business objectives and deliver meaningful insights.

One successful data product was a real-time analytics dashboard that provided key metrics for our sales team. Implementing it significantly improved our responsiveness to trends and customer behavior.

I utilize scripting in Python and leverage AWS services to automate repetitive tasks, which streamlines our workflows and reduces the potential for human error during data handling.

I prioritize projects based on business impact, stakeholder urgency, and resource availability, using project management tools to track progress and adjust timelines as needed.