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Capital Markets Data Integrity Specialist

2 weeks ago

Remote, India NLB Services Full time

About the Job

NLB Services is seeking a highly skilled Capital Markets Data Integrity Specialist to join our team. In this role, you will be responsible for ensuring the accuracy, completeness, and timeliness of critical capital markets data flows.

Key Responsibilities:

  1. Design and implement proactive (and reactive) data quality frameworks to meet high standards of accuracy, completeness, and timeliness.
  2. Collaborate with data engineers and business teams to identify key data quality issues, establish validation rules, and implement solutions that prevent data quality problems before they arise.
  3. Develop monitoring and automation tools to flag and address data issues in real-time.
  4. Leverage tools like Great Expectations or similar frameworks to validate data integrity across pipelines.
  5. Define data quality metrics and SLAs to measure performance and track progress.
  6. Pilot initial data flows to refine the framework, demonstrate value, and gain insights into how proactive data quality can support downstream data use cases.
  7. Drive data governance by building clear data ownership and stewardship responsibilities, processes for addressing data quality issues, and reporting tools for business and technical teams.

Requirements:

  • 7+ years of experience in data quality, data architecture, or a similar role in capital markets or financial services.
  • Proven experience designing and implementing data quality frameworks that ensure accuracy, completeness, and timeliness of data.
  • Expertise in data validation tools such as Great Expectations, whylogs, dbt expectations, Apache Griffin, or custom-built solutions for real-time data quality checks.
  • Strong skillset and experience in building full-stack applications in Python.
  • Understanding of cloud infrastructure (GCP or Azure) and ability to implement scalable data quality systems within cloud-native architectures.
  • Ability to work closely with data engineers to support their data pipeline development by embedding data quality checks and standards.
  • Experience establishing and tracking data quality metrics and SLAs, and working with teams to ensure compliance.