data
Posted Mar 4Senior Data Engineer
at CodeRoad
Latin America, United StatesOn-site
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Responsibilities
- Architecture Design: Lead the evolution of data warehouses, lakes, and streaming architectures to support business intelligence and analytics.
Requirements
- This role is open to experts in various on-demand technologies (Snowflake, Databricks, Redshift, etc.), requiring a focus on data integrity and high-performance processing.
- Your success is measured by the efficiency of data workflows, the reliability of data lakes/warehouses, and your ability to translate complex data
- Data Tech: Proficiency with modern data stacks (e.g., Spark, Airflow, Kafka, dbt) and cloud data warehouses (Snowflake, BigQuery, or Redshift).
- Languages: Advanced proficiency in SQL and Python or Scala. Cloud:
- Experience building data solutions on AWS, Azure, or GCP.
- Modeling: Strong understanding of data modeling techniques (Star Schema, Data Vault, etc.).
- Communication: Advanced English proficiency (written and verbal) for collaboration with US-based clients.
- experience in nearshore/offshore client-facing startup cultures. What you’ll love:
Experience
- Experience: 5+ years of dedicated data engineering experience.
Benefits
- Competitive Pay (USD)
- Holidays Off & Paid Time Off
- Health insurance assistance program
Additional details
- CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions.
- From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape. About the Role
- As a Senior Data Engineer, you will own the design and evolution of scalable data pipelines and architectures.
- You will act as the primary anchor for data ingestion, transformation, and storage solutions, ensuring that mission-critical data is accessible and reliable.
- requirements into technical reality. Key Responsibilities
- Pipeline Development: Design and build robust ETL/ELT processes to handle large-scale data ingestion and transformation.
- Data Quality: Implement rigorous testing and monitoring to ensure data accuracy, consistency, and security.
- Optimization: Manage and tune database performance, ensuring low-latency access for downstream applications.
- Team Influence: Collaborate with Data Scientists and Stakeholders to define data models and support the growth of junior engineers through technical mentorship. Requirements •
- Opportunity to work with high-growth US-based companies and cutting-edge tech stacks.