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About Me

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My name is Peter Luro. I’m a software engineer focused on building data systems that support financial workflows, trading processes, and large-scale data validation.

In my current role at Talcott Resolution, I design and maintain backend systems that ingest, validate, and reconcile financial datasets across Oracle, SQL Server, and cloud-based platforms. My work sits directly in the path of downstream reporting, hedging, and actuarial processes, where data accuracy and system reliability are critical.

I work primarily in Python and SQL, with experience building asynchronous pipelines, containerized jobs, and metadata-driven ingestion frameworks. Much of my work involves identifying inconsistencies across data sources, normalizing schemas, and ensuring that production workflows operate predictably under failure conditions.

Target Roles

  • Quantitative Engineer
  • Senior Software Engineer (Backend / Data)
  • Trading Systems Engineer
  • Market Data Engineer

Current Focus

  • Designing reliable data pipelines for financial datasets
  • Building validation and reconciliation systems across heterogeneous sources
  • Developing Python-based systems for market data analysis and signal generation
  • Expanding into quantitative engineering concepts including time-series analysis and trading system design

Selected Work

Financial Data Validation & Reconciliation

Built Python-based validation pipelines to compare and normalize datasets across Oracle and SQL Server environments. These systems identify schema drift, stale data, and mismatches before downstream reporting and risk workflows.

Azure-Based Data Processing Pipelines

Designed and deployed containerized jobs in Azure to process large financial datasets. Implemented retry logic, structured logging, and failure handling to ensure consistent execution across environments.

Trading System Development (Personal Project)

Developed a Python-based system that ingests intraday market data, evaluates breakout conditions, and executes rule-based trading logic. The system focuses on identifying high-momentum setups and managing execution constraints in real time.

Approach

I focus on building systems that are:

  • Reliable under failure conditions
  • Clear in their data contracts and assumptions
  • Scalable as data volume and complexity increase
  • Useful to downstream consumers without requiring manual intervention

Location

Open to opportunities in Boston, Florida, Utah, & Texas.