About Zintlr

Zintlr is an AI-powered B2B sales intelligence platform built in Bengaluru. We combine company and skilled data with AI-generated personality and buying-style insights, so sales and marketing teams can find the right accounts, understand their buyers, and run better outreach.

Role: Data Engineer – Data Acquisition & Integration

Experience: 2–3 years

Location: Bengaluru | Hebbal

Type: Full-time | On-Site The role

You'll build and run the pipelines that bring external data into Zintlr: public web sources, open datasets, partner feeds and third-party APIs. Your job is to turn that data into clean, reliable, well-documented datasets that our product and data science teams build on.

The work runs at scale, with millions of records and sources that change without warning, and quality is the bar. You'll own pipelines end to end, from scoping a new source through to monitoring it in production.

What you'll do

- Design, build and maintain Python pipelines that collect data from websites, public datasets, partner feeds and APIs.
- Scale ingestion to millions of records with scheduling, retries, logging and alerting.
- Deal with how the modern web actually works: JavaScript-rendered pages, pagination, authenticated partner integrations, and API rate limits, staying within them.
- Clean, normalise, deduplicate and validate data, and set up quality checks that catch regressions before they reach the product.
- Monitor jobs, debug failures, and keep pipelines healthy as sources change.
- Store and manage datasets across relational databases, search indexes and cloud storage.
- Make large ingestion jobs faster,



more accurate and cheaper to run.
- Document sources, pipelines and reusable components so others can build on them.
- Work with Data Science, Data Engineering and Product to evaluate and onboard new data sources.
- Follow Zintlr's data governance standards on source terms, privacy and access rules.

What we're looking for

- 2–3 years building data pipelines or data collection systems in Python.
- Hands-on experience with web data extraction frameworks such as Scrapy, Playwright, Selenium or BeautifulSoup.
- Working knowledge of HTML, CSS, JavaScript and browser developer tools, enough to understand how a page loads its data.
- Experience integrating REST APIs and third-party data sources: authentication, pagination, rate limits.
- SQL, plus at least one relational database (PostgreSQL or MySQL).
- Comfort with JSON, CSV, Excel and other structured formats.
- Concurrency in Python: multiprocessing, multithreading and asyncio.
- Production habits: error handling, logging, retries and tests.
- A solid grasp of data quality, meaning deduplication, validation and schema checks.
- Git and a standard code-review workflow.

Good to have

- Advanced browser automation with Playwright, run at scale.
- Document processing and OCR (e.g. Tesseract) for PDFs and scanned documents.
- AWS (EC2, S3, Lambda), Docker and Linux.
- Airflow or another workflow orchestration tool.
- Elasticsearch or MongoDB.
- ETL design and broader data engineering.
- Entity resolution, i.e. matching the same company or record across sources.
- CI/CD and deployment workflows.
- Data quality monitoring and automated testing for pipelines.

📌 Data Engineer (Bengaluru)
🏢 Zintlr
📍 Bengaluru