Data Acquisition Engineer: Build Scalable Pipelines

Polygon.io, Inc; United States; Hybrid; USD 110.000 - 170.000; Vollzeit

Benefits dieser Stelle

Medical plans

401(k)

Unlimited time off

Zusammenfassung

Massive is hiring a Data Acquisition Engineer to lead the ingestion, parsing, cleaning, and structuring of large external datasets used across our financial data platform. You will work with diverse inputs—from public datasets to financial reference data and alternative data—and turn them into clean, analysis-ready assets.

You will own the technical onboarding lifecycle: ingesting raw data, designing parsing logic, building reproducible ETL/ELT workflows, validating quality, and producing

Qualifikationen

• Direct experience with financial datasets at hedge funds or financial data providers.
• Proven track record onboarding or structuring large, complex datasets (market data, reference data, or alternative data).
• Strong Python skills for parsing, cleaning, and transformation; strong SQL for validation and modeling.

Aufgaben

• Own the technical onboarding of external datasets, parsing raw files and producing clean outputs.
• Write parsing and transformation logic in Python/SQL for CSV, JSON, XML, HTML, XBRL, PDF, etc.
• Develop reproducible ETL/ELT workflows to clean, normalize, and validate data.
• Manage storage and processing with S3-compatible object storage.
• Produce analytics-ready Parquet datasets with proper partitioning and metadata.
• Implement data-quality checks and handle schema drift and missing fields.
• Collaborate with internal teams to understand dataset characteristics and uses.
• Document dataset structure, parsing assumptions, and known limitations.

Kenntnisse

Python

SQL

ETL/ELT workflows

Data quality checks

S3-compatible storage

Parquet

Documentation

Metadata management

DataFusion/DuckDB

PDF data extraction

Jobbeschreibung

Massive is hiring a Data Acquisition Engineer to lead the ingestion, parsing, cleaning, and structuring of large external datasets used across our financial data platform. You will work with diverse inputs—from public datasets to financial reference data and alternative data—and turn them into clean, analysis-ready assets.

You will own the technical onboarding lifecycle: ingesting raw data, designing parsing logic, building reproducible ETL/ELT workflows, validating quality, and producing