Data Engineer

About MethodTech

MethodTech is building an institutional-grade investment technology platform for asset managers, portfolio managers, wealth managers, and other investment institutions.

Our platform brings together large-scale financial data, portfolio analytics, risk modelling, strategy research, portfolio construction, and investment workflows. Data infrastructure sits at the core of the platform, processing large volumes of market, fundamental, portfolio, and alternative financial datasets.

We are looking for a Data Engineer to help build and scale this infrastructure.

The Role

You will work closely with our engineering and quantitative research teams to build reliable data pipelines and infrastructure supporting financial datasets and analytics.

This is a hands-on engineering role with significant ownership. You will work on systems that ingest, clean, transform, validate, and serve large datasets used directly by our research and production applications.

Responsibilities

• Design, build, and maintain robust ETL/ELT pipelines for large financial datasets.
• Develop high-performance data processing workflows using Python and Pandas.
• Build and optimize pipelines using distributed computing frameworks where required.
• Integrate data from APIs, databases, files, and external data vendors.
• Design processes for data validation, reconciliation, monitoring, and error handling.
• Improve the performance, scalability, and reliability of existing data infrastructure.
• Build reusable data libraries and services consumed by research and application teams.
• Work with large historical and time-series datasets.
• Diagnose production data issues and improve observability of data pipelines.
• Collaborate directly with quantitative researchers and software engineers to translate data requirements into production systems.

Minimum Requirements

• Strong programming skills in Python.
• Strong working knowledge of Pandas and numerical/data-processing workflows.
• Experience designing and maintaining ETL/ELT pipelines.
• Experience with at least one distributed computing framework such as Dask, Spark, Ray, or similar.
• Strong understanding of databases, data structures, and efficient data processing.
• Comfortable working with large datasets and debugging complex data-quality issues.
• Strong problem-solving skills and ability to work independently.

Good to Have

• Experience with AWS or another major cloud platform.
• Experience with SQL, PostgreSQL, Parquet, S3, or similar technologies.
• Experience optimizing Python workloads for memory and compute efficiency.
• Familiarity with orchestration tools such as Airflow or Prefect.
• Experience with financial, market, or time-series data.
• Familiarity with Docker and production deployment environments.
• Knowledge of financial markets is helpful but not required.

What You'll Get

You will join a small, highly technical team where engineers have substantial ownership over the systems they build. Rather than maintaining a narrow component of a large organization, you will have the opportunity to design core infrastructure from the ground up and see it used directly in institutional investment workflows.

We value strong engineering fundamentals, intellectual curiosity, and people who enjoy solving difficult data problems.