Structured Data Engineer Sensitive Data Intelligence

Successful candidates will help transform large and complex data environments into accurate, defensible, and actionable outputs supporting cyber incident investigations, sensitive data assessments, legal and regulatory obligations, and potential breach notification activities. This role will focus on working with structured and semi-structured datasets, leveraging technologies such as Databricks, SQL, Python, and cloud-based data processing environments to identify, analyze, extract, and organize sensitive information.

About the Opportunity

*** is seeking experienced, hands-on Structured Data Engineers to support complex, large-scale sensitive data identification and extraction projects within our cybersecurity and incident response practice.

This role will focus on working with structured and semi-structured datasets, leveraging technologies such as Databricks, SQL, Python, and cloud-based data processing environments to identify, analyze, extract, and organize sensitive information.

Successful candidates will help transform large and complex data environments into accurate, defensible, and actionable outputs supporting cyber incident investigations, sensitive data assessments, legal and regulatory obligations, and potential breach notification activities.

We are seeking technically proficient professionals who can independently execute data processing and extraction workflows in fast-paced, deadline-driven environments.

Key Responsibilities

Data Processing and Engineering

• 
  
• Ingest, process, and analyze large volumes of structured and semi-structured data from diverse enterprise systems.
• 
  
• Develop and execute data processing workflows using Databricks, Apache Spark, SQL, and Python.
• 
  
• Work with relational databases, cloud data repositories, CSV files, JSON, Parquet, and other common data formats.
• 
  
• Design and optimize queries, transformations, and extraction workflows for performance and scalability.
• 
  
• Identify relationships across datasets using joins, keys, and other data-mapping techniques.
• 
  

Sensitive Data Identification and Extraction

• 
  
• Develop and execute workflows to identify personally identifiable information (PII), protected health information (PHI), financial information, and other sensitive data elements.
• 
  
• Extract and normalize relevant information from complex datasets to support sensitive data assessments.
• 
  
• Identify potentially impacted individuals and associate relevant sensitive data attributes with those individuals.
• 
  
• Perform deduplication, data validation, reconciliation, and quality control to improve the accuracy of extracted results.
• 
  
• Generate structured, person-level and record-level outputs supporting legal, regulatory, and client reporting requirements.
• 
  

Project Delivery and Quality Assurance

• 
  
• Collaborate with cybersecurity professionals, incident response teams, project managers, and legal stakeholders to understand project-specific data requirements.
• 
  
• Execute data processing and extraction activities against defined project timelines and deliverables.
• 
  
• Troubleshoot data quality issues, processing failures, and performance bottlenecks.
• 
  
• Maintain clear documentation of data sources, transformations, extraction logic, assumptions, and validation results.
• 
  
• Support defensible, repeatable workflows with appropriate quality controls and auditability.
• 
  
• Handle confidential and sensitive client information in accordance with applicable security and confidentiality requirements.
• 
  

Required Qualifications

• 
  
• Demonstrated professional experience in data engineering, database development, or large-scale data processing.
• 
  
• Strong proficiency in SQL, including complex queries, joins, transformations, and data manipulation.
• 
  
• Hands-on experience with Databricks, Apache Spark, or comparable distributed data processing platforms.
• 
  
• Proficiency in Python for data manipulation, automation, and extraction.
• 
  
• Experience working with large relational datasets and structured or semi-structured data sources.
• 
  
• Ability to independently troubleshoot technical challenges and optimize data processing workflows.
• 
  
• Experience producing accurate, validated datasets and structured outputs.
• 
  
• Strong attention to detail, documentation, and data quality.
• 
  
• Ability to work effectively under accelerated project timelines and changing requirements.
• 
  

Preferred Qualifications

• 
  
• Experience with Azure Databricks, Azure Data Lake Storage, or other cloud-based data platforms.
• 
  
• Familiarity with Delta Lake, PySpark, and distributed processing optimization.
• 
  
• Experience identifying or extracting PII, PHI, financial records, or other regulated information.
• 
  
• Prior involvement in cybersecurity incident response, data breach investigations, eDiscovery, forensic data processing, or sensitive data review.
• 
  
• Experience developing person-level data inventories, consolidated data subject outputs, or breach notification datasets.
• 
  
• Knowledge of data privacy and security requirements, including HIPAA, GDPR, and U.S. state data breach notification frameworks.
• 
  
• Experience with high-volume data processing, entity resolution, and automated quality assurance.
• 
  

What Success Looks Like

Successful contractors will be able to rapidly understand unfamiliar datasets, develop effective processing and extraction workflows, and deliver accurate, validated sensitive data outputs within demanding project timelines.

The ideal candidate combines strong technical execution with an understanding of data accuracy, confidentiality, defensibility, and the importance of reliable outputs in cybersecurity incident response.

Why Join ***

This opportunity offers the ability to contribute to complex, high-impact cybersecurity and sensitive data projects involving large-scale enterprise data environments.

Contractors will work alongside multidisciplinary teams addressing challenging data identification, processing, and reporting requirements for organizations navigating significant cybersecurity events.

About the Opportunity

*** is seeking experienced, hands-on Structured Data Engineers to support complex, large-scale sensitive data identification and extraction projects within our cybersecurity and incident response practice.

This role will focus on working with structured and semi-structured datasets, leveraging technologies such as Databricks, SQL, Python, and cloud-based data processing environments to identify, analyze, extract, and organize sensitive information.

Successful candidates will help transform large and complex data environments into accurate, defensible, and actionable outputs supporting cyber incident investigations, sensitive data assessments, legal and regulatory obligations, and potential breach notification activities.

We are seeking technically proficient professionals who can independently execute data processing and extraction workflows in fast-paced, deadline-driven environments.

Key Responsibilities

Data Processing and Engineering

• 
  
• Ingest, process, and analyze large volumes of structured and semi-structured data from diverse enterprise systems.
• 
  
• Develop and execute data processing workflows using Databricks, Apache Spark, SQL, and Python.
• 
  
• Work with relational databases, cloud data repositories, CSV files, JSON, Parquet, and other common data formats.
• 
  
• Design and optimize queries, transformations, and extraction workflows for performance and scalability.
• 
  
• Identify relationships across datasets using joins, keys, and other data-mapping techniques.
• 
  

Sensitive Data Identification and Extraction

• 
  
• Develop and execute workflows to identify personally identifiable information (PII), protected health information (PHI), financial information, and other sensitive data elements.
• 
  
• Extract and normalize relevant information from complex datasets to support sensitive data assessments.
• 
  
• Identify potentially impacted individuals and associate relevant sensitive data attributes with those individuals.
• 
  
• Perform deduplication, data validation, reconciliation, and quality control to improve the accuracy of extracted results.
• 
  
• Generate structured, person-level and record-level outputs supporting legal, regulatory, and client reporting requirements.
• 
  

Project Delivery and Quality Assurance

• 
  
• Collaborate with cybersecurity professionals, incident response teams, project managers, and legal stakeholders to understand project-specific data requirements.
• 
  
• Execute data processing and extraction activities against defined project timelines and deliverables.
• 
  
• Troubleshoot data quality issues, processing failures, and performance bottlenecks.
• 
  
• Maintain clear documentation of data sources, transformations, extraction logic, assumptions, and validation results.
• 
  
• Support defensible, repeatable workflows with appropriate quality controls and auditability.
• 
  
• Handle confidential and sensitive client information in accordance with applicable security and confidentiality requirements.
• 
  

Required Qualifications

• 
  
• Demonstrated professional experience in data engineering, database development, or large-scale data processing.
• 
  
• Strong proficiency in SQL, including complex queries, joins, transformations, and data manipulation.
• 
  
• Hands-on experience with Databricks, Apache Spark, or comparable distributed data processing platforms.
• 
  
• Proficiency in Python for data manipulation, automation, and extraction.
• 
  
• Experience working with large relational datasets and structured or semi-structured data sources.
• 
  
• Ability to independently troubleshoot technical challenges and optimize data processing workflows.
• 
  
• Experience producing accurate, validated datasets and structured outputs.
• 
  
• Strong attention to detail, documentation, and data quality.
• 
  
• Ability to work effectively under accelerated project timelines and changing requirements.
• 
  

Preferred Qualifications

• 
  
• Experience with Azure Databricks, Azure Data Lake Storage, or other cloud-based data platforms.
• 
  
• Familiarity with Delta Lake, PySpark, and distributed processing optimization.
• 
  
• Experience identifying or extracting PII, PHI, financial records, or other regulated information.
• 
  
• Prior involvement in cybersecurity incident response, data breach investigations, eDiscovery, forensic data processing, or sensitive data review.
• 
  
• Experience developing person-level data inventories, consolidated data subject outputs, or breach notification datasets.
• 
  
• Knowledge of data privacy and security requirements, including HIPAA, GDPR, and U.S. state data breach notification frameworks.
• 
  
• Experience with high-volume data processing, entity resolution, and automated quality assurance.
• 
  

What Success Looks Like

Successful contractors will be able to rapidly understand unfamiliar datasets, develop effective processing and extraction workflows, and deliver accurate, validated sensitive data outputs within demanding project timelines.

The ideal candidate combines strong technical execution with an understanding of data accuracy, confidentiality, defensibility, and the importance of reliable outputs in cybersecurity incident response.

Why Join ***

This opportunity offers the ability to contribute to complex, high-impact cybersecurity and sensitive data projects involving large-scale enterprise data environments.

Contractors will work alongside multidisciplinary teams addressing challenging data identification, processing, and reporting requirements for organizations navigating significant cybersecurity events.

[] Shift: []

Metasys Technologies/NVISH is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identify, national origin, veteran or disability status.