Data Engineering Platform Lead

2 days ago

Davao City, Davao, Philippines beBeeTechnicalLead Full time ₱1,500,000 - ₱2,000,000
About the Role

We are seeking a highly experienced technical lead to oversee the design, implementation and scaling of our end-to-end data platform.



Key Responsibilities
  • Owning the design and scalability of the data lake architecture for both streaming and batch workloads.
  • Leading the development of ingestion, transformation, and storage pipelines using AWS Glue, DMS, Kinesis/Kafka, and PySpark.
  • Structuring and evolving data into OTF 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 SLAs and SLOs 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.
  • 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.
  • Deep hands-on experience with AWS data stack: Glue, S3, Athena, Lake Formation, DMS, and Redshift Spectrum.
  • Expertise in designing data pipelines for real-time, streaming, and batch systems, including schema design, format optimization, and SLAs.
  • 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.
  • Experience implementing data access controls, encryption policies, and compliance workflows in regulated environments.
  • Ability to integrate GenAI tools into data engineering processes to drive measurable productivity and quality gains.
  • Demonstrated ability to lead teams, drive architectural decisions, and collaborate with cross-functional stakeholders.


Brownie Points
  • Experience working in a PCI DSS or any other central bank regulated environment with audit logging and data retention requirements.
  • Experience in the payments or banking domain, with use cases around reconciliation, chargeback analysis, or fraud detection.
  • Familiarity with data contracts, data mesh patterns, and data as a product principles.
  • Experience using GenAI to automate data documentation, generate data tests, or support reconciliation use cases.
  • Exposure to performance tuning and cost optimization strategies in AWS Glue, Athena, and S3.
  • Experience building data platforms for ML/AI teams or integrating with model feature stores.

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