Senior Software Engineer - Investment Data Platform

About Longaeva Partners

Longaeva: Pronounced "long-AY-vuh", our name is rooted in meaning! We are named after one of the oldest and most resilient living species, the longaeva pine. It represents longevity, adaptability, and persistence. It thrives in extreme conditions and has adapted for thousands of years in challenging environments. We hope to do the same, delivering positive, risk-adjusted returns to our investors, even in dynamic and demanding market conditions.

We are a New York based hedge fund with a global investment mandate focusing on long duration investing across long/short public equities and late-stage private deals. Our founder and CIO, Peter Goodwin, bringing nearly 20 years of investing experience across the healthcare, consumer and TMT sectors.

We are a high-conviction, ideas-driven firm built on collaboration, rigorous primary research, and data-driven decision making. A core part of our approach is leveraging advanced artificial intelligence and technology to enhance our research, generate insights, and drive better investment outcomes. At Longaeva, you'll join a team that values intellectual curiosity, diverse viewpoints, and a shared commitment to uncovering differentiated investment opportunities.

Overview

We are seeking a Senior Software Engineer - Investment Data Platform who loves data and thrives on building high-quality systems at scale. In this role, you will help build a next-generation, greenfield investment data platform that powers research, trading, risk, and portfolio management across the firm. You will work in an AI-augmented development environment, using modern AI coding tools (e.g., Claude Code, Codex, Copilot) as an integral part of your daily workflow to accelerate delivery while maintaining full ownership of design decisions, code quality, and production reliability.

You will be responsible for designing and building backend services, APIs, and reliable ingestion/processing frameworks for core investment datasets (security master, reference data, transactions, positions/exposures, P&L/returns, market data, and alternative data). You will partner closely with portfolio managers, traders, data scientists, and quantitative/risk teams to evolve solutions as business needs grow, and you will own critical production systems end-to-end-raising the bar on correctness, observability, and data trust. You will be expected to use AI tools and exercise strong engineering judgement.

Responsibilities

The Data Engineering team manages data pipelines, web scrapes, data warehouse/lakehouse foundations, and core data models across the organization. You'll join a highly dedicated team to build and maintain a world-class data platform for our hedge fund.

As a Senior Software Engineer, you will:

• Leverage AI tools to accelerate engineering
  • Use AI coding assistants as part of your daily development workflow to increase velocity on implementation, testing, and documentation tasks.
  • Exercise engineering judgment on when AI-generated output is appropriate and when manual development, deeper analysis, or architectural thinking is required.
  • Critically review and verify all AI-generated code for correctness, security, performance, and adherence to team standards.
• Build platform services & APIs
  • Design and build backend services and data APIs that expose curated investment datasets to downstream consumers (portfolio managers, risk, quantitative research, dashboards, and integrated tools).
  • Define and maintain clear data contracts, documentation, and versioning for platform interfaces.
• Own core investment data domains (front-to-back)
  • Security master and Reference data
  • Transactions, positions, and P&L
  • Market data, pricing, and benchmarks used for risk and performance
• Engineer reliable ingestion & processing
  • Design, implement, and operate scalable data pipelines to ingest, cleanse, normalize, and reconcile data from internal and external sources.
  • Implement robust data quality, lineage, monitoring, and reconciliation-especially for transactions, positions, and P&L-and continuously improve trust and transparency of the platform.
  • Improve datasets over time by adding sources, encoding business rules, and producing new metrics that support investment, trading, and risk decisions.
• Partner with the investment organization
  • Work directly with Investment, Trading, Risk, Quantitative Research, and Data Science teams to translate workflows into durable platform capabilities and data products.
  • Help package and disseminate data and analytics via integrated tools (APIs, dashboards, and automated reporting).

Required Qualifications

• 5+ years of strong software engineering experience building production systems with meaningful data complexity (buy-side experience strongly preferred).
• Track record of rapidly adopting new tools, frameworks, and workflows. Comfort operating in a fast-evolving environment where AI capabilities, best practices, and tooling are changing continuously.
• Expert knowledge of Python (and/or Java/Scala) and strong proficiency in SQL.
• Proficiency using AI coding assistants (e.g., Claude Code, Codex, Copilot) as part of a daily development workflow, including demonstrated judgment on when to leverage AI-generated output and when manual coding or deeper analysis is more appropriate.
• Proven experience building and operating production-grade data pipelines and data warehouse/lakehouse solutions.
• Strong understanding of core investment data domains: security master and reference data, transactions, positions/holdings/exposures, P&L/performance, and market data/pricing.
• Experience with modern platforms such as AWS, Snowflake, Spark, dbt.
• Familiarity with orchestration and containerization tools such as Airflow, Dagster, Prefect, Docker, and Kubernetes.
• Strong understanding of data modeling, partitioning/clustering, and performance tuning.
• End-to-end production ownership mindset: you take responsibility for what you ship, including data and code quality, monitoring, failure analysis, and system reliability - especially important as AI-assisted development accelerates the pace and blast radius of changes.