Data Engineer

As a Data Engineer at TRINETIX, your responsibilities encompass setting up data imports from external sources, constructing scalable data processing pipelines, and crafting data assets tailored for both experimental and organizational processes. You will work closely with engineers, data scientists, product managers, and business teams to ensure that all data products are fully aligned with our clients’ needs. This collaborative effort is essential to drive insights that support informed decision-making.

To qualify for the Data Engineer position at TRINETIX, you should ideally have a BS+ in computer science or equivalent experience along with a minimum of three years in Data Engineering and Python. Familiarity with the AWS stack—specifically Glue, Athena, EMR Serverless, and Redshift—is crucial, and experience with additional tools like Spark and PySpark will be a significant advantage. Strong collaboration skills and a willingness to learn are also essential.

In the Data Engineer role at TRINETIX, you will be working extensively with a wide range of technologies, particularly within the AWS ecosystem, including Glue, Athena, Kinesis, and Redshift. Additionally, knowledge of Spark, PySpark, Iceberg, Delta Lake, and various AWS services will be crucial. You'll have the opportunity to engage with cutting-edge tools, enhancing your technical skillset while contributing to innovative data projects.

At TRINETIX, you can expect a collaborative and dynamic work environment that fosters innovation and personal development. We prioritize comfortable working hours and offer a variety of employee benefits tailored to individual preferences. Additionally, you'll be part of a lively team, engaging in team events and knowledge-sharing opportunities that enhance both your technical abilities and interpersonal skills.

TRINETIX is committed to supporting employee growth and development through various means such as paid training programs, language courses, and mentoring opportunities. Our investment in your professional development helps ensure that you stay updated on the latest technologies and trends, positioning you for long-term success as a Data Engineer and within the broader technology landscape.

When answering this question, highlight specific projects where you used Python to clean, transform, or analyze large datasets. Detail the libraries you are familiar with, such as Pandas or NumPy, and how you applied them to solve data-related challenges.

Be prepared to discuss the big data technologies you've utilized, particularly those mentioned in the job description like Spark, AWS services, or any relevant tools. Explain the context of your experience and the outcomes achieved in your previous projects.

A strong response would include methods you use for data validation, monitoring, and error handling. Detail particular strategies or tools implemented to maintain data integrity, such as automated testing or continuous integration methods.

You should describe the characteristics of a scalable data pipeline, such as the ability to handle increasing data volumes without a drop in performance. Discuss any personal experiences where you designed or maintained scalable systems and the metrics used to measure success.

When addressing this question, provide examples of how you’ve successfully worked with different stakeholders in previous roles. Emphasize your communication skills, adaptability, and how you ensure alignment on project goals in collaborative environments.

Share specific challenges you have encountered in the field, explaining the context and how you tackled these situations with innovative solutions. Highlight your problem-solving skills and what you learned in the process.

Share a detailed story about a particular project involving AWS services, describing what you built, the services used, challenges faced, and the impact of your work on the organization or client.

Discuss your technical approach to data ingestion, including the tools or frameworks you prefer, and specific use cases where you've successfully implemented these strategies for effective data pipeline setup.

Express your commitment to continuous learning. Mention specific resources you follow, such as industry blogs, webinars, or online courses, and give examples of how you’ve applied new knowledge to your work.

Reflect on your career aspirations, whether they involve technical specialization, moving into leadership roles, or embracing new technologies. This question allows you to connect your goals with the company's objectives, showing you’re aligned with TRINETIX's vision.