Associate, Data Engineer

As an Associate Data Engineer at Global Atlantic Financial Group, your main responsibilities include expanding and optimizing data pipelines, conducting data architecture analysis, and collaborating with cross-functional teams to support data strategy initiatives. You'll also work on designing enterprise data solutions, ensuring optimal data delivery, and delivering applications and services critical to business operations.

To qualify for the Associate Data Engineer role at Global Atlantic, candidates must have a bachelor's degree in Computer Science or a related field, along with at least eight years of experience in the IT sector. Key skills include data modeling and processing, SQL expertise, and familiarity with languages and tools like Python and Hadoop.

At Global Atlantic Financial Group, Associate Data Engineers work with a variety of technologies. Familiarity with Java, J2EE, Hadoop, Spark, Scala, Python, and SQL is crucial, as these tools are central to optimizing data pipelines and developing enterprise applications.

The work environment at Global Atlantic Financial Group is fast-paced and collaborative. Associates are encouraged to innovate and contribute to a culture of teamwork. You'll typically work 40 hours a week in a professional setting, contributing to meaningful projects that make a lasting impact in the life insurance and retirement space.

The interview process for the Associate Data Engineer position at Global Atlantic typically involves multiple stages, including initial phone screenings, technical interviews, and interviews with management. Candidates may be assessed on their technical skills, problem-solving abilities, and fit within the company culture.

When answering this question, highlight specific projects where you were involved in data architecture design and implementation. Detail the technologies used, challenges faced, and how your contributions improved data management processes.

Discuss your proficiency in languages relevant to the position—especially Java, Python, and SQL. Cite practical examples where you've utilized these languages in previous roles to build or enhance data pipelines.

Explain your systematic approach to data pipeline optimization, which may include analyzing performance bottlenecks, implementing efficient data structures, or using appropriate ETL tools. Describe a scenario where your optimization efforts led to measurable improvements.

Be prepared to discuss your familiarity with big data technologies such as Hadoop and Spark. Share specific examples of how you've leveraged these tools to handle large data sets or complex data processing tasks.

Highlight your experience with performing data quality checks, implementing data validation rules, and conducting root cause analyses. Mention any tools or methodologies you've used to maintain data integrity throughout the data lifecycle.

Share a specific example of a data challenge you faced, describing the situation, your thought process, and the final solution. This response should showcase your problem-solving skills and technical expertise.

Discuss the various ways you keep your skills sharp—whether through online courses, attending workshops, or following industry experts. Showing a commitment to continuous learning can significantly impress interviewers.

Describe your communication strategies and how you adapt your technical jargon for different stakeholders. Provide examples of effective collaborations you've had with business partners and other teams.

Talk about the SDLC methodologies you have worked with, such as Agile or Waterfall. Detail how you have implemented these methodologies in your past projects to ensure timely and efficient delivery of data solutions.

Provide your insights on contemporary challenges such as data privacy, managing large datasets, or integrating new technologies. Discuss how you believe these challenges can be addressed in the data engineering field.