Our Client is building a data platform where product, analytics, and customer-facing decisions rely on accurate, consistent, and timely data.

As we scale, ensuring the integrity of data across pipelines, transformations, and downstream systems becomes critical.

Role Overview

You will work across the data stack to ensure that data flowing through our systems remains reliable, consistent, and aligned with business expectations.

This involves working closely with data pipelines, transformations, and analytical layers to identify inconsistencies, investigate anomalies, and improve overall data quality.

Responsibilities

- Work with data pipelines across ingestion, transformation, and warehouse layers
- Investigate discrepancies in metrics, dashboards, and downstream data systems
- Trace data issues across multiple stages (source - transformation - output)
- Collaborate with product and analytics teams to understand data usage and expectations
- Improve reliability of data systems through validation, monitoring, and process improvements
- Contribute to improving how data issues are identified, understood, and resolved

What We’re Looking For

- Experience working with data pipelines and transformations (e.g., Airflow, Spark/PySpark, DBT, SQL-based workflows)
- Experience working on cloud platforms (GCP or AWS)




- Robust SQL and working knowledge of Python
- Experience debugging issues in production data systems
- Familiarity with data warehouses (Big Query, Snowflake, Redshift, etc.)

Strong Signals

- Experience working on data that supports product, growth, or business reporting
- Ability to reason about how data changes impact metrics and downstream use cases
- Experience working with large datasets and understanding their structure and behavior
- Comfortable collaborating with both engineering and non-technical stakeholders

What You’ll Work On

- Investigating unexpected changes in key metrics
- Understanding inconsistencies across dashboards and data sources
- Improving trust in data used by product and business teams
- Ensuring data systems scale reliably as usage grows

Ideal Backgrounds

- Data Engineers working on analytics or product data
- Analytics Engineers working with transformation layers (DBT, SQL models)
- Engineers who have worked closely with business or product teams on data-driven systems

Why This Role

You’ll be working at the intersection of data engineering and product impact, where understanding data correctness is as important as building the systems themselves.

📌 Data Engineer – Data Quality & Analytics (India)
🏢 a Snaphunt Client
📍 India