Data Engineer (Python & AWS)

About you:

You are a Data Engineer who enjoys building reliable, scalable, and high-performance data platforms that drive meaningful impact. You thrive in designing end-to-end solutions, applying software engineering best practices, and leveraging cloud technologies to deliver clean, efficient, and automated data pipelines. You are collaborative, detail-oriented, and constantly seeking to improve and innovate within the modern data ecosystem.

You Bring to Applaudo the Following Competencies:

• Bachelor’s degree in Computer Science, Data Engineering, or related field, or equivalent practical experience.
• 5+ years of hands-on experience as a Data Engineer.
• Advanced proficiency in SQL and strong understanding of database fundamentals.
• Proficient in Python for data processing, automation, and transformation.
• Experience working with AWS, Azure, or GCP, including core data services.
• Strong knowledge of data modeling (dimensional, normalized, and performance-optimized).
• Experience with workflow orchestration tools (Airflow, Dagster, or Prefect).
• Familiarity with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation.
• Understanding of software engineering best practices, including CI/CD, version control, and testing.
• Experience with Docker or Kubernetes for containerized data solutions (nice to have).
• Knowledge of Apache Spark or similar distributed data processing frameworks (nice to have).
• Familiarity with streaming technologies such as Kafka, Kinesis, or Pub/Sub (nice to have).
• English is a requirement, as you will be working directly with US-based clients.

You Will Be Accountable for the Following Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines integrating multiple data sources (APIs, databases, streams, and files).
• Develop and optimize cloud-based data warehouses and data lakes (Snowflake, BigQuery, Redshift, etc.).
• Implement observability and monitoring for data pipelines, including logging and alerting.
• Apply software engineering principles to ensure reliable, reusable, and well-documented code.
• Collaborate with cross-functional teams to align data infrastructure with analytics and business goals.
• Ensure data reliability, scalability, and performance across environments.
• Automate workflows and promote Infrastructure as Code for consistency and repeatability.
• Stay current with emerging data engineering tools and technologies and propose continuous improvements.