We are a multinational team of individuals who believe that with the right knowledge and approach, technology is the answer to the challenges that business face today. We have been bringing this knowledge and approach to our customers since 2016, helping them translate technology into their success.

With Swiss roots and our own development team in Lima and throughout the region, we offer the best of both cultural areas: the talent and passion of Latin American professionals combined with Swiss organization skills and mindset.

As a Data Engineer, you will play a pivotal role in our organization's data ecosystem. Your primary responsibilities will revolve around the collection, transformation, and management of large-scale data, leveraging a variety of tools and technologies to support business intelligence, data analytics, and machine learning initiatives.

This opportunity involves working as a full-time independent contractor in a completely remote capacity. The client is in the process of forming a nearshore team and involved in manufacturing and marketing American consumer products worldwide. While the hiring is facilitated through Bertoni, you will collaborate directly with the end client of our US-based partner.
 
Location: The client is located in the United States, but the position is for 100% remote work.
Contract/Project Duration: Initially 3 months with extension intend based on performance.
Time Zone and Schedule: Full-time, Monday to Friday (8 hours per day, 40 hours per week), Pacific Time Zone (PST).
 
Interview steps:

• Interview with Bertoni Solutions
• Interview & live coding with partner
• 1 or 2 interviews with the client 

Key Responsibilities:

Data Processing and Modeling

• Utilize a wide range of technologies, Python, SQL, for data processing and modeling.
• Leverage experience with ETL tools in a cloud-based environment, such as Azure Data Factory or similar tools in other cloud providers, for efficient data processing.
• Manage data stored in cloud storage locations, like Azure Data Lake Storage (ADLS) or similiter tools in other cloud providers for data extraction, transformation, and loading.

Data Loading and Transformation

• Handle data preprocessing and loading of JSON files (real-time streaming), Parquet, and text files (batch) using cloud-based tools.
• Employ parallel programming techniques to enhance data loading performance.
• Implement Python programs following an object-oriented programming (OOP) approach.

Data Processing and Analysis

• Perform big data analysis on POS (Point of Sale) and E-commerce data, focusing on PySpark within a Databricks environment.

• Bachelor’s or master’s degree in a related field (e.g., Computer Science, Data Engineering, or a relevant STEM discipline).
• Experience in consumer products/retail-related data, with a deep understanding of data sources, analytics, and specific challenges in this industry.
• Proven experience in data engineering, including working with big data technologies such as Snowflake, Hadoop, and PySpark in a Databricks environment.
• Strong proficiency in data processing and modeling with tools like Python, SQL.
• Knowledge of database design, performance optimization, and ETL processes in cloud-based environments.
• Experience working with cloud storage locations like Azure Data Lake Storage (ADLS) and ETL tools such as Azure Data Factory or equivalent in other cloud providers.
• Familiarity with parallel programming for data processing and data loading.
• Strong problem-solving skills and an ability to work collaboratively in a team.
• Advanced written and spoken English.

• Strong communication and collaboration skills to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders.
• Demonstrated ability to keep up to date with evolving data engineering and cloud technologies, showcasing a commitment to ongoing learning and professional development.
• Strong attention to data quality and security best practices to ensure the integrity and privacy of sensitive data.
• A commitment to documentation and maintaining clear, concise records of data engineering processes, making them accessible for team members.
• Understanding of data governance and compliance, including knowledge of relevant regulations and best practices.
• Excellent problem-solving skills and the ability to troubleshoot and optimize data pipelines for efficiency and performance.