Data Engineer

Latin America, United StatesOn-site

Requirements

Infrastructure: Build the infrastructure required for optimal extraction, transformation, and loading (ETL) of data from a wide variety of sources using SQL and Cloud technologies (Azure/GCP). •
Communication: Advanced English (B2-C1, spoken and written). •
Experience with Databricks, Python, and SQL (Scala is a plus). •
Big Data Tools: Familiarity with Apache Spark, Data Factory, Synapse, or BigQuery. • Data Pipelines:
Experience building Big Data pipelines via Streams and/or Batches. •

Analytical Skills: Strong skills related to working with unstructured datasets and performing root cause analysis to identify opportunities for improvement. What you’ll love: • 100% Remote • Holidays Off • Paid Time Off •
Health insurance assistance program •
Competitive Pay (USD) •

At Coderoad , we're more than just a software development company—we're your gateway to the global tech world.
Whether you're looking to skill up or level up your career, we offer the challenges you’ve been searching for.
We provide end-to-end software development services and give you the opportunity to work on exciting, real-world projects in a supportive environment.
Whether it's staff augmentation, dedicated IT teams, or general software engineering, we have opportunities for everyone to challenge themselves and take their career to the next level! The Role
You will work with stakeholders to assist with data-related technical issues and support their data infrastructure needs. •
Position Location: Remote (Latin America). • Time Zone
Requirements: This team operates on US East/West Coast time zones . Candidates must be willing to adjust schedules to meet specific project needs.
Pipeline Architecture: Create and maintain optimal data pipeline architecture. •
Data Assembly: Assemble large, complex data sets that meet functional and non-functional business requirements. •
Process Improvement: Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability. •