Data QA Engineer

Dataart Technologies; Hyderabad; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Dataart Technologies is seeking a senior data quality engineer in Hyderabad to own end-to-end data quality for ETL pipelines. You will design tests, publish metrics, and validate movements from source systems into analytical datasets using SQL on Snowflake models.

The role requires hands-on Python automation with pytest, Postgres validation, and experience with Airflow for orchestrating pipelines. Join a team focused on data reliability and scalable quality gates.

Qualifications

• Senior-level data quality engineering experience in ETL testing and data validation.
• Strong SQL and Snowflake data-modelling experience, including layered raw, history, silver, and gold models, reconciliation and drift queries.
• Experience validating PostgreSQL sources and replication.
• Hands-on Python automation for data reconciliation using pytest.
• Experience with orchestrated pipelines in Airflow.
• Experience assessing downstream impact of schema changes.

Responsibilities

• Own data quality for data moving from source systems into analytical datasets.
• Design, implement, and execute data quality tests and publish quality metrics and dashboards.
• Validate end-to-end pipelines (movement, transformation, completeness, history, freshness, compliance).
• Write reconciliation, drift, and quality SQL on Snowflake layered models.
• Validate PostgreSQL sources with consistent snapshot counts and replication checks.
• Use data models to assess downstream impact and set quality coverage.
• Automate data comparison and reconciliation in Python with pytest and keep the checks in GitHub.

Skills

Senior-level data quality engineering

ETL testing

Data validation

SQL

Snowflake data modelling

Data contracts

Python automation

pytest

Airflow

GitHub

Tools

Airflow

GitHub

Job description

Role & responsibilities

• Own data quality for data moving from source systems into analytical datasets, from test design through execution, investigation, and reporting.


• Design, implement, and execute data quality tests, and publish quality metrics and dashboards that producing and consuming groups can use.


• Validate pipelines end to end, covering movement, transformation, completeness, history, freshness, and compliance.


• Write reconciliation, drift, and quality SQL on Snowflake layered models (raw, history, silver, and gold), including slowly changing dimensions and merge behavior.


• Validate PostgreSQL sources with consistent snapshot counts and checks that logical replication kept the expected data.


• Use logical and physical data models and data contracts to assess downstream impact and set quality coverage.


• Automate data comparison and reconciliation in Python with pytest, and keep the checks in GitHub.

Preferred candidate profile

• Senior-level data quality engineering experience in ETL testing and data validation.


• Strong SQL and Snowflake data-modelling experience, including layered raw, history, silver, and gold models, reconciliation and drift queries, and slowly changing dimensions and merge behavior.


• Experience with logical and physical data models and with assessing the downstream impact of schema and data-contract changes.


• Experience validating PostgreSQL sources, including consistent snapshot counts and logical replication.


• Hands-on Python automation for data comparison and reconciliation, using pytest or an equivalent framework.


• Experience running and reading orchestrated pipelines in Airflow or an equivalent scheduler.


• Experience validating data movement, transformation, completeness, history, freshness, and compliance from source systems into analytical datasets.