Senior Data Engineer - Scale Financial Data Pipelines

McNeil & Co.; Metro Manila; On-site; PHP 1,800,000 - 3,000,000; Full time

Job summary

Arch Investment Management Ltd. (AIML) is expanding its Risk & Quantitative Strategies team and is seeking a senior data engineer to own and scale the data platform underpinning our systematic credit strategies.

You will architect production data pipelines, ingest diverse financial datasets, collaborate with quant researchers to translate needs into robust data systems, and uphold data governance and quality in a fast-paced, high-stakes environment.

Qualifications

• 5+ years of professional data engineering experience and ownership of production data platforms.
• Strong Python and SQL proficiency with production data pipelines.
• Experience with cloud platforms and multi-environment deployments.
• Experience with CI/CD and observability for production systems.
• Familiarity with FastAPI or similar APIs and data quality testing.
• Excellent communication and ability to work with researchers and engineers.

Responsibilities

• Architect, build, and own production data pipelines and storage.
• Collaborate with quantitative researchers to translate needs into data systems.
• Ensure data quality, testing, and validation across pipelines.
• Implement CI/CD, monitoring, and observability practices.
• Document data flows and governance controls.
• Partner with engineers to deliver scalable data architectures.

Skills

Python

SQL

Cloud platforms

ETL pipelines

CI/CD

Observability/Monitoring

FastAPI

Data quality

Data governance

Communication

Education

BS in Engineering

BS in Computer Science

BS in Information Technology

Tools

Snowflake

Databricks

GitHub Actions

Job description

Arch Investment Management Ltd. (AIML) is expanding its Risk & Quantitative Strategies team and is seeking a senior data engineer to own and scale the data platform underpinning our systematic credit strategies.

You will architect production data pipelines, ingest diverse financial datasets, collaborate with quant researchers to translate needs into robust data systems, and uphold data governance and quality in a fast-paced, high-stakes environment.