Salary
≈ $30k – $52k per year (Estimated)
Location
In office (Vijayawada)
Seniority
Staff · 10+ years exp
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 5, 2026.
Overview
Company
Impact
Profile match
HCLTech is a major Indian multinational information technology (IT) services and consulting company headquartered in Noida, Uttar Pradesh. Spun off from the original HCL Group in 1991, it ranks as one of India's largest technology companies alongside firms like TCS, Infosys, and Wipro.
Job Summary
Experience - 10+ years of experience Must have Skills: Databricks, Python, Data masking Good to have Skills: IICS, Informatica TDM Job Description:
- Configure and maintain Data masking projects on Databricks
- Design and implement masking policies using Databricks (deterministic, random, shuffling, substitution, key-based, format-preserving), ensuring consistent masking across systems.
- Implement and manage Data Subset strategies (constraint-based, rule-based) and optimize extracts for performance and storage.
- Build and schedule data flow for end-to-end orchestration (extract, mask, validate, publish), including dependencies, parameters, and notifications.
- Perform post-mask validation (row counts, referential integrity checks, key uniqueness, format checks) and reconcile issues with source/application teams.
- Monitor and troubleshoot the job execution (logs, performance bottlenecks, failures) and drive incident/problem resolution through ServiceNow.
- Ensure compliance with data privacy and audit requirements (least privilege access, evidence capture, approvals, retention/cleanup of test extracts).
- Create and maintain reusable masking rule libraries, templates, and standardized runbooks to improve repeatability across applications.
- Coordinate releases and stakeholder communication: refresh windows, downtime impacts, sign-offs, and delivery confirmations.
- Design and build data pipelines using Python to provision test datasets from enterprise sources.
Key Responsibilities
Experience - 10+ years of experience Must have Skills: Databricks, Python, Data masking Good to have Skills: IICS, Informatica TDM Job Description:
- Configure and maintain Data masking projects on Databricks
- Design and implement masking policies using Databricks (deterministic, random, shuffling, substitution, key-based, format-preserving), ensuring consistent masking across systems.
- Implement and manage Data Subset strategies (constraint-based, rule-based) and optimize extracts for performance and storage.
- Build and schedule data flow for end-to-end orchestration (extract, mask, validate, publish), including dependencies, parameters, and notifications.
- Perform post-mask validation (row counts, referential integrity checks, key uniqueness, format checks) and reconcile issues with source/application teams.
- Monitor and troubleshoot the job execution (logs, performance bottlenecks, failures) and drive incident/problem resolution through ServiceNow.
- Ensure compliance with data privacy and audit requirements (least privilege access, evidence capture, approvals, retention/cleanup of test extracts).
- Create and maintain reusable masking rule libraries, templates, and standardized runbooks to improve repeatability across applications.
- Coordinate releases and stakeholder communication: refresh windows, downtime impacts, sign-offs, and delivery confirmations.
- Design and build data pipelines using Python to provision test datasets from enterprise sources.
Skill Requirements
Experience - 10+ years of experience Must have Skills: Databricks, Python, Data masking Good to have Skills: IICS, Informatica TDM Job Description:
- Configure and maintain Data masking projects on Databricks
- Design and implement masking policies using Databricks (deterministic, random, shuffling, substitution, key-based, format-preserving), ensuring consistent masking across systems.
- Implement and manage Data Subset strategies (constraint-based, rule-based) and optimize extracts for performance and storage.
- Build and schedule data flow for end-to-end orchestration (extract, mask, validate, publish), including dependencies, parameters, and notifications.
- Perform post-mask validation (row counts, referential integrity checks, key uniqueness, format checks) and reconcile issues with source/application teams.
- Monitor and troubleshoot the job execution (logs, performance bottlenecks, failures) and drive incident/problem resolution through ServiceNow.
- Ensure compliance with data privacy and audit requirements (least privilege access, evidence capture, approvals, retention/cleanup of test extracts).
- Create and maintain reusable masking rule libraries, templates, and standardized runbooks to improve repeatability across applications.
- Coordinate releases and stakeholder communication: refresh windows, downtime impacts, sign-offs, and delivery confirmations.
- Design and build data pipelines using Python to provision test datasets from enterprise sources.
Other Requirements
Experience - 10+ years of experience Must have Skills: Databricks, Python, Data masking Good to have Skills: IICS, Informatica TDM Job Description:
- Configure and maintain Data masking projects on Databricks
- Design and implement masking policies using Databricks (deterministic, random, shuffling, substitution, key-based, format-preserving), ensuring consistent masking across systems.
- Implement and manage Data Subset strategies (constraint-based, rule-based) and optimize extracts for performance and storage.
- Build and schedule data flow for end-to-end orchestration (extract, mask, validate, publish), including dependencies, parameters, and notifications.
- Perform post-mask validation (row counts, referential integrity checks, key uniqueness, format checks) and reconcile issues with source/application teams.
- Monitor and troubleshoot the job execution (logs, performance bottlenecks, failures) and drive incident/problem resolution through ServiceNow.
- Ensure compliance with data privacy and audit requirements (least privilege access, evidence capture, approvals, retention/cleanup of test extracts).
- Create and maintain reusable masking rule libraries, templates, and standardized runbooks to improve repeatability across applications.
- Coordinate releases and stakeholder communication: refresh windows, downtime impacts, sign-offs, and delivery confirmations.
- Design and build data pipelines using Python to provision test datasets from enterprise sources.
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