Cloud Data Engineer: Spark, AWS & Lakehouse

Blend360; Región Centro; On-site; MXN 400,000 - 700,000; Full time

Benefits offered by this job

Christmas Bonus

Saving Fund

Life Insurance with funeral coverage

Medical Insurance including dental and

Vision plan

Job summary

Blend360 is seeking a Data Engineer Analyst to contribute to its next level of growth. You will design, build, and maintain scalable data pipelines and lake architectures on AWS, leveraging Spark, Iceberg, and serverless services for efficient data processing.

Collaboration with data analysts and platform engineers is essential to deliver production-ready solutions while ensuring data quality and security across the data platform.

Qualifications

• Advanced hands-on experience with AWS services (Glue, S3, Athena, IAM).
• Spark / PySpark for data processing.
• Data lake architectures, ideally with Iceberg.
• Data ingestion patterns: batch, streaming, CDC.
• SQL for data querying, validation, exploration.
• Data modeling and large-scale processing.
• Strong communication with non-technical stakeholders.

Responsibilities

• Design, build, and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and CDC.
• Develop and optimize workflows using Spark and PySpark for large datasets.
• Implement and manage data lake architectures on AWS with S3 and Iceberg.
• Work with Glue, Athena, and Lambda for ETL and querying.
• Manage data access controls using AWS IAM and ensure least-privilege.
• Collaborate with analysts, scientists, and engineers to meet data needs.
• Monitor pipelines, troubleshoot data quality, and improve reliability.
• Document data flows, architecture decisions, and standards.

Skills

AWS Glue

Amazon S3

Amazon Athena

AWS IAM

Spark / PySpark

Iceberg

Data lakes

CDC patterns

SQL

Data modeling

Airflow

dbt

Step Functions

Infrastructure as Code

AI-assisted development

Tools

Airflow

dbt

Step Functions

Job description

Blend360 is seeking a Data Engineer Analyst to contribute to its next level of growth. You will design, build, and maintain scalable data pipelines and lake architectures on AWS, leveraging Spark, Iceberg, and serverless services for efficient data processing.

Collaboration with data analysts and platform engineers is essential to deliver production-ready solutions while ensuring data quality and security across the data platform.