Senior Data Engineer

EQL Global; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

EQL Global is seeking a Data Acquisition Engineer to design and operate high-throughput pipelines that ingest public disclosures from thousands of sources. You will build resilient systems for large-scale data collection, parsing diverse formats, and ensuring data quality.

You will work with cloud platforms, containers, and modern orchestrators to scale coverage across markets while maintaining compliance with data governance standards.

Qualifications

• Strong Python engineering with experience in scalable data pipelines.
• Experience with large-scale, parallel data collection systems.
• Proficiency in parsing HTML/PDF/XBRL/XML and extracting structured data.

Responsibilities

• Architect and maintain distributed data acquisition pipelines capable of 10,000+ filings concurrently.
• Build fault-tolerant extraction systems with retry logic and rate-limit handling.
• Develop parsers to convert unstructured documents into clean data.
• Implement monitoring, data quality checks and alerting.

Skills

Python

Scrapy

Playwright

Selenium

asyncio

Celery

Kafka

RabbitMQ

AWS

Kubernetes

Airflow

GDPR

Tools

Docker

Airflow

Job description

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 aData Acquisition Engineerto 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.

This is a high-throughput, distributed-systems challenge. 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.

What You'll Do

• Architect and maintaindistributed data acquisition pipelinescapable of retrieving and processing10,000+ filings concurrentlyfrom public regulatory and exchange sources.
• Build fault-tolerant extraction systems with robust retry logic, rate-limit handling, request orchestration, and proxy/session management.
• 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.

What We're Looking For

• StrongPythonengineering, with hands-on experience in frameworks such as Scrapy, Playwright, Selenium, requests/httpx, or equivalent.
• Proven experience buildinglarge-scale, parallelized data collection systems— async programming (asyncio/aiohttp), concurrency, and queue-based architectures (Celery, Kafka, RabbitMQ, or similar).
• Practical mastery of structured extraction: 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 approachto data collection — respecting source terms, public-data boundaries, and data-governance standards (GDPR, etc.).

Nice to Have

• 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.

Why EQL

• Work on a genuinely hard, large-scale engineering problem at the core of the product.
• Join an early-stage team where your architecture decisions have real, lasting impact.
• Build infrastructure that institutional clients depend on every day.
• Remote-friendly culture with a Stockholm base.