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Data Validation Engineer with AI(Databricks & DataStage)

Quadrant Technologies -
Remote

Quick apply

Job details

Full-time
₹60 - ₹65 an hour

Qualifications

Performance tuning
DataStage
Law
Data lake
Master's degree
SQL
AWS
Scala
Unity
ETL
S3
Metadata
Redshift
AI
Python
Debugging

Full job description

Role Summary:

We are seeking a Data Validation Engineer with Testing with strong hands-on experience building and optimizing data pipelines on Databricks. This role focuses on implementation, performance, and reliability while working within a well-defined architectural framework.

Key Responsibilities:

Develop and maintain data pipelines on Databricks using medallion architecture principles
Build, optimize, and maintain Databricks notebooks for ingestion, transformation, and analytics
Implement data flows across boundary systems ensuring reliability, scalability, and data quality
Apply performance tuning techniques including query optimization, cluster sizing, and Delta Lake optimization
Work with Unity Catalog to manage schemas, permissions, and data access controls
Collaborate closely with architects to implement approved data designs and standards
Troubleshoot pipeline failures, data issues, and performance bottlenecks
Ensure code quality, documentation, and operational readiness of data solutions

Required Qualifications:

Strong hands-on experience with Databricks and Delta Lake
Strong hands-on experience with AWS services such as S3, EC2, Lambda, Glue, and Redshift.
Working experience with IBM DataStage for ETL development, data integration, and data transformation.
Proven ability to design metadata-driven test harnesses that scale — parameterized test configs that can run against 500 jobs today and 80K tomorrow without rearchitecting
Experience with data validation tooling (Great Expectations, Deequ, or custom PySpark validation libraries) for row count, schema, transformation logic, and output comparison checks
Performance and throughput benchmarking — rows/sec, latency comparisons between legacy (DataStage) and target (Databricks) pipelines
Experience with Iceberg Tables
Proven experience implementing medallion architecture in real world use cases
Experience building notebooks using Python, SQL, or Scala
Solid understanding of data integration across multiple systems
Strong performance tuning and debugging skills in Databricks environments
Experience working in enterprise scale data platforms
Hands-on experience with any of the Databricks AI/ML ecosystem components, including Mosaic AI, MLflow, Unity Catalog, Feature Engineering, Vector Search, Databricks Model Serving, and Delta Lake.
Knowledge of Databricks AI Functions and SQL AI capabilities.
Experience implementing RAG pipelines using Delta Lake, Unity Catalog, and Vector Search.
Familiarity with LLMOps, model monitoring, evaluation, and governance using MLflow.

Pay: ₹60.00 - ₹65.00 per hour

Experience:

Databricks: 5 years (Required)
DataStage: 4 years (Required)
AI: 3 years (Required)
AWS: 3 years (Preferred)
ETL: 4 years (Preferred)
Data Validation: 5 years (Preferred)
Iceberg Tables: 3 years (Preferred)
Pyspark: 5 years (Preferred)
Data lake: 4 years (Preferred)
RAG Pipelines: 3 years (Preferred)
LLM: 3 years (Preferred)

Work Location: Remote

Quick apply

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data engineer – enterprise data platform & analytics jobs in remote

Data Validation Engineer with AI(Databricks & DataStage)

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Software Engineer | AI Platforms

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