Data Engineer - job 1 of 2

A Data Engineer at Gemini is responsible for designing, automating, building, and launching scalable data pipelines using Python. You will build and enhance dimensional models for our Data Warehouse, collaborate closely with data analysts to understand requirements, and develop systems that allow teams to interpret data intuitively. It's essential to perform root cause analysis for any data issues and to maintain documentation for processes, making your role pivotal in ensuring data integrity and efficiency.

To qualify for the Data Engineer role at Gemini, you should have a minimum of 4 years of experience in data engineering, particularly with data warehouse technologies and custom ETL design. Advanced proficiency in Python and SQL is mandatory, alongside experience with MPP databases like Redshift or Snowflake. Additionally, strong communication skills and a solid understanding of data structures and algorithms are crucial for effective collaboration across teams.

At Gemini, Data Engineers enjoy a flexible hybrid work environment, particularly for those located within 30 miles of our New York City or Seattle offices. Employees are encouraged to collaborate in person two days a week to enhance productivity. For those outside of metropolitan areas, the remote-first approach allows for greater flexibility while still being part of the innovative and diverse culture that Gemini fosters.

Gemini is committed to the professional growth of its Data Engineers by providing opportunities for mentorship and continuous skill development. As a Data Engineer, you will have the chance to lead projects, research new technologies, and enhance your expertise while working alongside cross-functional teams. The environment encourages learning and development, ensuring that you stay at the forefront of industry trends.

Data Engineers at Gemini can look forward to a comprehensive benefits package, including a competitive salary, discretionary annual bonuses, and long-term incentive equity grants. Other perks include comprehensive health plans, a 401K with company matching, flexible time off, and paid parental leave. We believe in supporting our employees both professionally and personally, fostering a well-rounded work-life balance.

The ETL process, which stands for Extract, Transform, and Load, is crucial in data engineering as it enables the organized movement of data from multiple sources into a data warehouse or database. During an interview, emphasize how you design ETL processes to ensure data accuracy and efficiency, and discuss your experience with various ETL tools and methodologies that you've employed in previous projects.

Optimizing SQL queries can significantly improve application performance and reduce load times. Discuss techniques such as indexing, avoiding SELECT *, using WHERE clauses effectively, and analyzing execution plans. Share specific examples from your experience where you successfully tuned queries to enhance performance.

In responding to this question, highlight your experience in designing data models, particularly the creation of star and snowflake schemas for data warehousing. Illustrate how you’ve applied dimensional modeling principles to organize and structure data for analytical purposes, which directly supports business decision-making.

Data validation is essential in ensuring the accuracy and quality of data being processed. In your answer, detail your approaches to testing data integrity and the validation methods you've implemented, like creating comprehensive test plans and scripts to catch errors before they impact downstream systems.

Addressing data quality issues promptly is vital in data engineering. Describe a structured approach where you first conduct root cause analysis to identify where the errors occurred, and then outline the steps taken to rectify those issues while also implementing preventative measures to avoid future occurrences.

Discuss your knowledge of cloud environments and services relevant to data engineering, such as AWS, Google Cloud, or Azure. Highlight specific projects where you utilized cloud technologies for data storage, processing, or analytics, and explain how cloud infrastructure can enhance scalability and agility in data engineering tasks.

Collaboration is vital in a dynamic work environment like Gemini. Emphasize your communication and teamwork strategies, including coordinating with data analysts, product managers, and other stakeholders to ensure everyone has clear expectations and deliverables, which ultimately leads to successful project outcomes.

Use this question to showcase a specific project where you faced challenges, such as integrating disparate data sources or improving data pipeline efficiency. Detail the steps you took to overcome those challenges and the positive outcomes generated from your efforts.

While Python and SQL are essential for a Data Engineer, you may also use other languages depending on the project needs. During the interview, discuss your proficiency in Python for data manipulation, SQL for database queries, and any other languages you may have used, illustrating how each contributes to your effectiveness in data engineering.

The world of data engineering is constantly evolving. Share the resources you use to stay informed, such as online courses, webinars, tech blogs, or industry conferences. Highlight any communities you may participate in or any insider knowledge you have gained that helps you remain at the forefront of the data engineering field.