Data Engineer

Dunboxed is a boutique data services firm specializing in Data Analytics, Business Intelligence (BI), and Data Engineering. We don't just deliver services; we build strategic partnerships that drive real business value for our clients. As a growing firm, we are agile, innovative, and focused on quality. Every team member plays a critical role in our success.

The role:

We are seeking a highly skilled and versatile Data Engineer to join our consulting team. In this client-facing role, you will play a key role in designing, building, and maintaining data infrastructure for a diverse portfolio of clients. You will collaborate directly with client stakeholders and our internal teams to ensure data is collected, processed, and made available for analysis and reporting. This position requires strong technical acumen, excellent communication skills, and the ability to adapt to different cloud environments and technologies.

What You'll do:

• Data Pipeline Development:

  Design, develop, and maintain scalable data pipelines that process data from various sources for client projects.

• Multi-Cloud Integration:

  Leverage major cloud platforms such as

  AWS, Azure, or Google Cloud

  to build and optimize data solutions, ensuring efficient storage, access, and security based on client needs.

• Python/PySpark Expertise:

  Utilize Python and/or PySpark for data transformation, manipulation, and complex ETL processes, writing clean and efficient code.

• Data Modeling:

  Create and maintain data models that align with client business requirements, ensuring data accuracy and reliability.

• Client Collaboration:

  Work directly with data scientists, analysts, and business stakeholders on the client-side to understand their data requirements and deliver reliable datasets.

• Performance and Quality:

  Implement data quality checks and performance optimizations in data pipelines and queries to ensure fast and efficient processing.

Required Qualifications:

• Proven experience as a Data Engineer, with a focus on cloud-based solutions.
• Strong proficiency in

  Python and/or PySpark

  for data processing and ETL tasks.
• Experience with cloud platforms such as

  AWS, Azure, or Google Cloud

  .
• Knowledge of data warehousing concepts (e.g., Redshift, BigQuery) and data modeling principles.
• Excellent problem-solving skills and strong communication skills to work effectively in a client-facing role.
• Experience with

  DBT

  (Data Build Tool) for managing data transformations.
• Relevant cloud certifications (e.g., AWS Certified Data Analytics, Azure Data Engineer).