Data VerificationValidation Engineer

Hyderabad - India

Monthly Salary: Not Disclosed

Experience Required: 0-1years

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

This is a remote position.

As a Data Verification/Validation Engineer youll ensure the accuracy completeness consistency and reliability of data used across Krivaras analytics automation and AI/ML solutions. You will design and run validation checks build automated test suites for data pipelines investigate anomalies and partner with Data Engineering Analytics and ML teams to keep datasets production-ready.

Key responsibilities

Define data quality standards (completeness validity uniqueness timeliness consistency) aligned to business/solution requirements.
Build and maintain automated data validation frameworks for ETL/ELT pipelines and lake/warehouse layers.
Create rule-based and statistical checks (schema validation constraints ranges referential integrity outlier detection).
Develop test plans and test cases for datasets transformations and data products (dashboards ML features reports).
Perform root-cause analysis for data issues (pipeline breaks upstream changes mapping errors duplicates drift).
Implement monitoring & alerting for data health (SLAs freshness volume anomaly thresholds).
Partner with stakeholders to document data definitions lineage and acceptance criteria.
Support release validation for data pipeline changes and ensure production readiness.
Contribute to continuous improvement: reusable checks QA best practices performance optimizations and audit readiness.

Requirements

Bachelors degree in Computer Science IT Data Engineering Statistics or equivalent experience.

0-2 years of experience in data QA / data validation / ETL testing / analytics engineering.

Strong SQL (joins window functions profiling queries reconciliation).

Experience validating data in data warehouses/lakes (e.g. Snowflake/BigQuery/Redshift/Synapse/Databricks or similar).

Hands-on scripting in Python (preferred) or another language for automation.

Understanding of ETL/ELT concepts incremental loads CDC basics transformations and pipeline orchestration.

Familiarity with data quality dimensions and how to quantify them with metrics and dashboards.

Solid debugging skills and ability to communicate issues clearly with evidence (queries samples lineage notes).

Benefits

The skills are transferable: The skills you acquire will never be out of use.
Work anywhere in India.
Constant learning curve: You will only get better with passing time as you will be in sync with the technological changes.
You can be as creative as you want: Each day you create something from nothing. The only thing that limits you will be your imagination.

Required Skills:

Data Verification Python SQL

Required Education:

CS/IT related fields

This is a remote position. As a Data Verification/Validation Engineer youll ensure the accuracy completeness consistency and reliability of data used across Krivaras analytics automation and AI/ML solutions. You will design and run validation checks build automated test suites for data pipelines...

This is a remote position.

Key responsibilities

Define data quality standards (completeness validity uniqueness timeliness consistency) aligned to business/solution requirements.
Build and maintain automated data validation frameworks for ETL/ELT pipelines and lake/warehouse layers.
Create rule-based and statistical checks (schema validation constraints ranges referential integrity outlier detection).
Develop test plans and test cases for datasets transformations and data products (dashboards ML features reports).
Perform root-cause analysis for data issues (pipeline breaks upstream changes mapping errors duplicates drift).
Implement monitoring & alerting for data health (SLAs freshness volume anomaly thresholds).
Partner with stakeholders to document data definitions lineage and acceptance criteria.
Support release validation for data pipeline changes and ensure production readiness.
Contribute to continuous improvement: reusable checks QA best practices performance optimizations and audit readiness.