Senior Data Engineer (AWS) at CodeRoad

Modena, Emilia-Romagna, Italy -

Full Time

Start Date

Immediate

Expiry Date

02 Oct, 26

Salary

0.0

Posted On

04 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Dbt Cloud, Snowflake, Terraform, Docker, AWS S3, AWS Glue, AWS Athena, AWS EventBridge, AWS AppFlow, AWS DMS, Dimensional Modeling, SCD Type 2, Power BI, AWS Lambda

Industry

E-Learning Providers

Description

AWS Data Engineer Latin America | 100% Remote About CodeRoad 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 We are looking for a Mid-to-Senior Data Engineer to architect, build, and operate cloud-native data pipelines on AWS. In this role, you will anchor the flow of data from diverse source systems—including relational databases, SaaS applications, and event streams—into a central Snowflake data platform modeled in dbt Cloud. You will be responsible for ensuring technical excellence across our data infrastructure by writing production-grade Python, utilizing modern AI coding assistants like GitHub Copilot and Claude, and managing infrastructure as code via Terraform. This role is critical to shaping and defending our core data architecture, optimizing platform performance, and delivering curated, analytics-ready datasets for downstream BI and reporting. As a key contributor on a fully remote team, your autonomy, ownership mindset, and clear written communication will directly impact our ability to scale data solutions and maintain full historical accuracy using advanced dimensional modeling techniques. Key Responsibilities Design and own the architectural evolution of data pipelines and platform components, leading design reviews and defending technical decisions with evidence-based reasoning. Build and maintain robust ingestion pipelines leveraging AWS DMS, Amazon AppFlow, and Amazon EventBridge to land data seamlessly into AWS S3 and Snowflake. Optimize and model analytics datasets within dbt Cloud, applying strict dimensional modeling (Kimball techniques) and implementing SCD Type 2 logic. Lead performance tuning and cost optimization efforts across the Snowflake ecosystem, proactively implementing monitoring, alerting, and root-cause analysis. Collaborate with cross-functional teams to prepare curated datasets for Power BI, while mentoring peers through rigorous code reviews and engineering enablement. Requirements Years of Experience: 5+ years of professional experience in Data Engineering roles. Tech Stack: Proficiency with Python (including AWS Lambda), advanced SQL, dbt Cloud, Snowflake, Terraform (IaC), and Docker. Cloud Infrastructure: Deep hands-on experience with core AWS data services (S3, Glue, Athena, EventBridge, AppFlow, DMS). Data Modeling: Solid grasp of dimensional modeling (star schemas, fact/dimension design) and practical implementation of SCD Type 2. Soft Skills: High autonomy, ownership mindset, discipline to raise risks early, and comfort utilizing AI-assisted development tools (GitHub Copilot, Claude). Language: Advanced English (written and spoken) is mandatory for seamless remote collaboration. Nice to Have AWS Certified Data Engineer – Associate (DEA-C01) or equivalent certification. Experience with change-data-capture (CDC) and near-real-time ingestion patterns. Exposure to workflow orchestration tools such as Apache Airflow, Dagster, or Prefect. Familiarity with data observability frameworks (Great Expectations, Monte Carlo) and open table formats like Apache Iceberg. What You’ll Love 100% Remote Holidays off Paid Time Off Health insurance assistance Competitive USD compensation Growth opportunities

Responsibilities

Architect and operate cloud-native data pipelines on AWS to move data into a Snowflake platform modeled in dbt Cloud. Responsible for optimizing platform performance, implementing dimensional modeling, and delivering analytics-ready datasets for BI reporting.