Backend Engineer, Data Engineering

Role Overview

• Design, build, and maintain scalable data pipelines that ingest, normalize, and transform financial and clinical trial data from multiple internal and external sources, with a focus on making data suitable for analytics, reporting, and LLM-based AI agents.
• Develop backend services and data access layers that expose high-quality financial data to internal systems and customer-facing features, ensuring data is structured for direct consumption by LLMs and automated workflows.
• Implement and operate embedding pipelines and vectorized representations of structured and semi-structured data to support semantic search, RAG, and agentic workflows.
• Optimize database performance, query execution, and batch processing jobs to support large-scale financial datasets and AI-driven access patterns.
• Participate in architectural decisions around data infrastructure, storage strategies, and integration with LLMs, embeddings, and vector databases.
• Work closely with product managers and analysts to translate business requirements into durable datasets, metrics, and APIs.
• Contribute to frontend implementations to ensure data-heavy and AI-enabled features are presented clearly and correctly.
• Mentor other engineers on data modeling best practices, SQL performance optimization, ETL design patterns, and building reliable, observable data systems.

Requirements

• 8+ years of professional software engineering experience, with a strong focus on backend development and data engineering.
• Deep expertise in SQL and relational database design (PostgreSQL, MySQL, or similar), including complex analytical queries and performance tuning.
• Experience building and operating ETL or ELT pipelines using orchestration frameworks (Airflow, Dagster, Prefect, or AWS Step Functions).
• Production experience building AI-powered data systems, including vector databases (pgvector, Pinecone), embedding pipelines, and designing data access patterns for RAG and agentic workflows.
• A pragmatic approach to the full stack, including a willingness to contribute to React codebases to ensure data-heavy features are delivered end-to-end.
• Familiarity with data quality validation, testing, and monitoring practices in production systems.
• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.
• Strong proficiency in Python, with experience using web frameworks (Django, DRF).
• Experience working with modern data warehouse platforms (Snowflake, BigQuery, Redshift).
• Experience integrating backend systems or data pipelines with LLM APIs for enrichment, summarization, or analysis.
• Experience with data transformation and validation tools (dbt, Great Expectations).
• Experience building production AI features end-to-end, including LLM integration, prompt engineering, agentic workflows, and observability/evaluation frameworks.
• Experience working in a Series A-C startup with rapid scale.

Tech Stack

• Airflow
• Amazon Redshift
• AWS
• BigQuery
• Django
• ETL
• MySQL
• Postgres
• Python
• React
• SQL

Benefits

• Competitive compensation and meaningful equity participation
• Comprehensive employee benefits, including 100% company-paid health, dental, vision, and life insurance
• 401(k) plan with a 3% company match that vests immediately
• Unlimited PTO