Data Acquisition Engineer - Build Analytics-Ready Financial Data

Massive; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Massive is seeking a Data Acquisition Engineer to lead ingestion, parsing, cleaning, and structuring of large external datasets for our financial data platform based in Atlanta. You will design parsing logic, develop cleaning and normalization workflows, and produce analysis-ready outputs across diverse sources.

You will collaborate with data and research teams to understand dataset characteristics, ensure data quality, and implement robust ETL/ELT pipelines that scale with a growing data

Qualifications

• Direct experience working with financial datasets at a hedge fund, financial institution, or major financial data provider.
• Proven track record onboarding or structuring large, complex financial or finance-adjacent datasets, such as market data, fundamentals, regulatory data, reference data, or alternative data.
• Strong proficiency in Python for parsing, cleaning, and transformation workflows.
• Strong SQL skills for exploration, validation, and modeling.
• Hands-on experience working with S3-compatible object storage for large dataset management.
• Proficiency with Parquet and other columnar storage formats, including partitioning strategies for performance and scale.
• Experience designing ETL/ELT workflows that are reproducible, maintainable, and resilient to upstream dataset changes.
• Ability to interpret messy or loosely documented datasets and design stable parsing logic.
• Clear written communication skills for documenting processes, assumptions, and dataset behavior.
• Experience with DataFusion, DuckDB, or other modern analytical engines is a plus.
• Exposure to datasets such as regulatory filings, financial reference data, or alternative data is a plus.
• Experience extracting structured information from PDFs or other irregular data sources is a plus.
• Familiarity with schema validation, metadata management, or data-quality frameworks is a plus.

Responsibilities

• Own the technical onboarding of external datasets, including parsing raw files, transforming fields, and producing clean structured outputs.
• Write parsing and transformation logic in Python and SQL to handle diverse file formats (CSV, JSON, XML, HTML, XBRL, PDF, etc.).
• Develop reproducible ETL/ELT workflows that clean, normalize, validate, and structure incoming datasets.
• Manage data storage and processing workflows using S3-compatible object storage systems.
• Produce efficient, analytics-ready Parquet datasets, using appropriate partitioning and metadata conventions.
• Implement data-quality checks to detect anomalies, schema drift, missing fields, or unexpected changes in incoming data.
• Troubleshoot and resolve inconsistencies through systematic, transparent cleaning and transformation rules.
• Collaborate with internal data and research teams to understand dataset characteristics, quirks, semantics, and intended uses.
• Provide light technical input during dataset evaluation, offering insight into ingest feasibility and transformation complexity.
• Write clear documentation describing dataset structure, parsing assumptions, transformation logic, and known limitations.

Skills

Python

SQL

ETL/ELT

Data quality

S3 storage

Parquet format

DataFusion

PDF extraction

Documentation

Dataset onboarding

Financial data

Schema validation

Job description

Massive is seeking a Data Acquisition Engineer to lead ingestion, parsing, cleaning, and structuring of large external datasets for our financial data platform based in Atlanta. You will design parsing logic, develop cleaning and normalization workflows, and produce analysis-ready outputs across diverse sources.

You will collaborate with data and research teams to understand dataset characteristics, ensure data quality, and implement robust ETL/ELT pipelines that scale with a growing data