Data Ingestion Pipelines for Fintech

We're an early-stage fintech startup building a platform that turns messy, unstructured source data into structured, decision-grade insights. We're looking for a data engineer to help build the ingestion and tooling layer that powers it: pulling from disparate sources, extracting structured signals, and resolving and stitching records across sources into a coherent view. What you'll do Build and maintain data ingestion pipelines across a range of source types (APIs, scraped/crawled content, documents, feeds) Develop extraction tooling that pulls structured insights from unstructured inputs Design entity resolution logic to match and merge records across sources Own data quality, monitoring, and pipeline reliability Must-haves Strong data engineering background (pipeline orchestration, ETL/ELT, schema design) Experience working with unstructured and semi-structured data Comfort with data analysis and validating output quality Fluency in Python and SQL Nice-to-haves Fintech, financial data, or regulated-data experience Entity resolution / record linkage experience Familiarity with tools like Dagster and LLM-based extraction