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Middle Data Engineer (MongoDB) – LATAM

Latin America

About Distillery

Distillery Tech is committed to diversity and inclusion. We actively seek to cultivate a workforce that reflects the rich tapestry of perspectives, backgrounds, and experiences present in our society. In our recruitment efforts, we are dedicated to promoting equal opportunities for all candidates, regardless of race, ethnicity, gender, sexual orientation, disability, age, or any other dimension of diversity.

Distillery accelerates innovation through an unyielding approach to nearshore software development. The world’s most innovative technology teams choose Distillery to help accelerate strategic innovation, fill a pressing technology gap, and hit mission-critical deadlines. We support essential applications, mobile apps, websites, and eCommerce platforms through the placement of senior, strategic technical leaders and by deploying fully managed technology teams that work intimately alongside our client’s in-house development teams. At Distillery, we’re not here to reinvent nearshore software development; we’re on a mission to perfect it.
 

About the Position

As a Data Engineer (MongoDB), you will work closely with our product management and technology teams to solve complex business challenges and ensure the delivery of effective and comprehensive technical solutions. You will be instrumental in documenting requirements, developing user stories, facilitating testing processes, and acting as a bridge between business and technical teams. Your role will ensure that projects are executed smoothly and meet the needs of stakeholders while maintaining high standards of quality and documentation.
 

Responsibilities

  • Design and implement MongoDB aggregation pipelines using stages such as $match, $group, $project, and $sort to transform and analyze complex datasets.

  • Clean and prepare data for analysis in Tableau by creating aggregation pipelines that perform data cleaning, filtering, and transformation.

  • Identify and resolve performance bottlenecks in MongoDB by optimizing queries, implementing effective indexing strategies, and applying best practices for performance tuning.

  • Proficiently debug and troubleshoot issues within aggregation pipelines to ensure smooth and reliable data processing.

  • Handle and transform JSON data to meet specific analytical and reporting requirements.

  • Document your work and adhere to MongoDB best practices and architectural guidelines to ensure maintainable and sustainable solutions.
     

Requirements

  • Extensive experience in developing and optimizing MongoDB aggregation pipelines, with a deep understanding of MongoDB’s Aggregation Framework.

  • Strong knowledge of MongoDB stages (e.g., $match, $group, $project, $sort) and their application in data transformation and analysis.

  • Proficiency in JavaScript or Node.js.

  • Experience in cleaning and preparing data for analysis in Tableau, including the use of aggregation pipelines for data transformation.

  • Familiarity with MongoDB features, architecture, and performance tuning techniques, including query optimization and indexing strategies.

  • Demonstrated experience in manipulating and transforming JSON data.

  • Proven ability to debug and troubleshoot issues with aggregation pipelines.

  • Highly organized with a keen attention to detail and strong follow-through skills.

  • Ability to work effectively in a team environment and adapt to new technologies.

Nice to Have

  • Experience with additional data visualization tools or platforms.

  • Familiarity with Scrum teams and agile methodologies.

  • Proficiency in using advanced features and tools within MongoDB.

Why You'll Like Working Here

  • A generous, competitive compensation package for exceptional performers, including a generous vacation package and competitive benefits plan.

  •  A remote working environment that promotes flexibility and work-life balance.

  • Opportunities for professional and personal development, including access to cutting-edge technologies and methodologies.