Data Acquisition Engineer EQL Global · Stockholm (Remote-friendly) · Full time · Engineering
About EQL Global EQL Global is a compliance-first equity data and AI workflow platform serving institutional buy-side and sell-side clients across the Nordics, the UK, and Europe. Our coverage spans 33,000+ listed companies across 89 countries, and our data infrastructure powers analyst workflows at firms that demand accuracy, freshness, and full auditability.
We are building the regulated data layer for institutional capital markets — and the foundation of that layer is the ability to acquire, parse, and structure vast volumes of public corporate disclosure at speed and at scale.

The Role We're looking for a Data Acquisition Engineer to design and operate the systems that ingest public financial disclosures — regulatory filings, annual reports, prospectuses, exchange notices, and structured datasets — from thousands of sources worldwide.
On any given run, our pipelines need to retrieve and process tens of thousands of documents in parallel without dropping data, tripping rate limits, or compromising integrity. If you've built resilient, large-scale data collection systems and you care about doing it cleanly and compliantly, we want to talk to you.

Architect and maintain distributed data acquisition pipelines capable of retrieving and processing 10,000+ filings concurrently from public regulatory and exchange sources.
Develop parsers that turn unstructured and semi-structured documents (HTML, PDF, XBRL, XML) into clean, validated, structured data.
Engineer monitoring, alerting,



and data-quality checks so we catch gaps, schema drift, and source changes before our clients do.
Optimize throughput and cost across cloud infrastructure while keeping collection respectful of source-side constraints.
Work closely with our data and product teams to expand coverage across new markets and document types.

Strong Python engineering, with hands-on experience in frameworks such as Scrapy, Playwright, Selenium, requests/ or equivalent.
Proven experience building large-scale, parallelized data collection systems — async programming (asyncio/aio concurrency, and queue-based architectures (Celery, Kafka, RabbitMQ, or similar).
XPath, CSS selectors, regex, and parsing of PDF/HTML/XBRL/XML at scale.
Experience with rate-limit handling, proxy rotation, session management, and building resilient pipelines against unreliable or changing sources.
Comfort with cloud infrastructure (AWS/GCP/Azure), containerization, and orchestration (Docker, Kubernetes, Airflow, or similar).
A disciplined, compliance-aware approach to data collection — respecting source terms, public-data boundaries, and data-governance standards (GDPR, etc.).

Familiarity with financial disclosure formats (XBRL/iXBRL, SEC EDGAR, ESEF, exchange filing systems).
Experience with data validation and observability tooling.
Background in fintech, financial data vendors, or capital markets.
Working knowledge of Swedish and/or other European languages.

Remote-friendly culture with a Stockholm base.
Apply through LinkedIn or reach out directly.

📌 Senior Engineer, Data Engineering (Bengaluru)
🏢 EQL Global
📍 Bengaluru