Senior Cloud Data Architect

1 week ago

Barcelona, Philippines beBeeDataEngineer Full time ₱1,200,000 - ₱1,500,000
Job Summary

We are seeking a seasoned data engineer to architect and implement an end-to-end data platform built on AWS services. The successful candidate will lead a small team of engineers while working cross-functionally with stakeholders from fraud, finance, product, and engineering to enable reliable, timely, and secure data access across the business.


About the Role
  • Owning the design and scalability of the data lake architecture for both streaming and batch workloads, leveraging AWS-native services such as Glue, Lake Formation, and DMS.
  • Leading the development of ingestion, transformation, and storage pipelines using AWS Glue, DMS, Kinesis/Kafka, and PySpark.
  • Structuring and evolving data into open table formats (Apache Iceberg, Delta Lake) to support real-time and time-travel queries for downstream services.
  • Driving data productization, enabling API-first and self-service access to curated datasets for fraud detection, reconciliation, and reporting use cases.
  • Defining and tracking key performance indicators (KPIs) for critical data pipelines, ensuring high availability and data accuracy in a regulated fintech environment.
  • Collaborating with InfoSec, SRE, and Data Governance teams to enforce data security, lineage tracking, access control, and compliance regulations.
  • Using generative AI tools to enhance developer productivity – including auto-generating test harnesses, schema documentation, transformation scaffolds, and performance insights.
  • Mentoring data engineers, setting technical direction, and ensuring delivery of high-quality, observable data pipelines.

Requirements
  • At-least 7 years of experience in data engineering with expertise in designing data pipelines for real-time, streaming, and batch systems.
  • Deep hands-on experience with AWS data stack: Glue (Jobs & Crawlers), S3, Athena, Lake Formation, DMS, and Redshift Spectrum.
  • Strong programming skills in Python (PySpark) and advanced SQL for analytical processing and transformation.
  • Proven experience managing data architectures using open table formats at scale.
  • Understanding of stream processing with Kinesis/Kafka and orchestration via Airflow or Step Functions.
  • Ability to integrate generative AI tools into data engineering processes to drive measurable productivity and quality gains.

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