Senior Data Engineer Lead

7 hours ago

San Fernando, Central Luzon, Philippines beBeeData Full time $150,000 - $200,000
Job Opportunity

As a senior technical lead in data engineering, you will design, develop, and scale our end-to-end data platform built on AWS services.



Responsibilities
  • Owning the architecture of the data lake for streaming and batch workloads using native AWS services.
  • Leading the development of ingestion, transformation, and storage pipelines with Glue, DMS, Kinesis/Kafka, and PySpark.
  • Structuring and evolving data into formats like Iceberg, Delta Lake to support real-time and time-travel queries for downstream services.
  • Driving productization of data, enabling API-first access to curated datasets for fraud detection, reconciliation, and reporting use cases.
  • Defining SLAs and SLOs for critical pipelines, ensuring high availability and data accuracy in a regulated environment.
  • Collaborating with InfoSec, SRE, and Data Governance teams to enforce data security, lineage tracking, access control, and compliance.
  • Using AI tools to enhance developer productivity through auto-generated 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.
  • 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 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 AI 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.

Preferred Qualifications
  • Experience working in a PCI DSS or 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 AI to automate data documentation, generate data tests, or support reconciliation use cases.
  • Exposure to performance tuning and cost optimization strategies in AWS services.
  • Experience building data platforms for ML/AI teams or integrating with model feature stores.

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