Middle Data Engineer

As a Middle Data Engineer at Infinite Lambda, your primary responsibilities will include SQL programming, data transformation using Python, and engaging in data modeling and ETL/ELT processes. You will work on BI tools, contributing insights from data warehousing solutions, while possibly leading initiatives in ML and MLOps.

For the Middle Data Engineer role at Infinite Lambda, you need 3 to 5 years of relevant experience, proficiency in SQL, and familiarity with Python and machine learning frameworks. Additionally, experience with BI tools, knowledge of data warehousing solutions like Snowflake or Databricks, and familiarity with AWS, GCP, or Azure are highly recommended.

Candidates for the Middle Data Engineer position at Infinite Lambda should possess strong technical skills, including fluency in SQL, proficiency with Python for data transformation, and familiarity with ETL/ELT processes. Knowledge of BI tools, cloud providers like AWS and Azure, as well as experience in ML and MLOps, is also essential for success.

The Middle Data Engineer position at Infinite Lambda offers a hybrid work arrangement. This means you can work both remotely and in the office, with the flexibility to manage your schedule while being a vital part of a distributed team.

At Infinite Lambda, as a Middle Data Engineer, you can expect a culture that celebrates diversity and promotes open communication. Our inclusive environment allows team members to be their true selves, emphasizing collaboration, agility, and respect throughout our diverse teams.

When answering this question, emphasize specific SQL tasks you've performed, such as database queries or data manipulation. Discuss the complexity of the projects and any specific tools you used to achieve results, showcasing your fluency and practical application of SQL.

In your response, highlight libraries like Pandas, NumPy, or even scikit-learn when discussing machine learning. Provide examples of how these libraries helped you transform data and solve complex problems in past projects.

Narrate a specific instance, detailing the tools and technologies you used, your role in the process, and the outcomes of your implementation. Focus on your problem-solving abilities and the impact of your work on overall project success.

Talk about your strategies for maintaining data quality, such as validation checks, data cleaning techniques, or regular audits. Give examples from past projects to illustrate how these methods have led to successful outcomes.

Identify various use cases for BI tools, such as data visualization, reporting, and analytics. Specify your familiarity with tools like Looker, Tableau, or Power BI, and share examples of how you used these tools to drive business decisions.

Discuss specific challenges such as performance issues, data cleansing, or complexity of analyses. Share how you've approached these challenges, demonstrating your problem-solving skills and ability to deliver results under pressure.

Detail projects you've completed using cloud providers, focusing on specific services you utilized, such as databases, data storage, or machine learning tools. Highlight the benefits achieved and any skills you developed from those experiences.

Explain your communication methods, tools, and practices for effective collaboration in remote settings. Provide examples of how open dialogue and respect have led to success in your past distributed team roles.

Describe techniques you employ, such as staying on top of sprint goals, keeping task lists, or using project management tools. Illustrate how you effectively balance multiple responsibilities within agile frameworks.

Share your strategies for continuous learning, including online courses, community involvement, or attending relevant conferences and webinars. Demonstrate your commitment to staying up-to-date in this field.