Data Quality Analysts

Overview

We are seeking a highly skilled Data Quality Analyst with 3 to 6 years of experience and a strong foundation in both business and technical expertise in Investment Banking Domain. The ideal candidate will be an analytical thinker, adept at problem-solving, reviewing data quality checks, and proposing new ones. As a Data Quality Analyst, you will ensure the accuracy, consistency, and reliability of data within the organization.

Key Responsibilities

Perform comprehensive data quality checks and propose enhancements to existing processes

Analyze and solve complex problems related to data quality in the Investment Banking domain

Collaborate with cross-functional teams to ensure data integrity across various systems

Support Business System Analysis ,Project Management, and Quality Assurance initiatives

Work on Trade Life Cycle and Management modules, including Front, Middle, and Back- office operations

Utilize tools such as Charles River Investment Management System (CRD), SQL for data analysis

Engage in extended testing areas, including End to End Performance Testing

Conduct Data Validation and Cleansing to identify and rectify errors and inconsistencies, ensuring high data quality

Continuously monitor data quality metrics and trends to pinpoint areas needing improvement

Perform Data Quality Assessments and audits to evaluate data quality

Collaborate closely with data engineers, data scientists, and other stakeholders to uphold data quality standards

Maintain detailed documentation of data quality processes and standards

Create and present reports on data quality metrics and improvements to management

Required Skills

Strong expertise in investment banking, capital markets -

Front, Middle and Back office operations

Proficient in SQL, ETL and various data analysis tools

Well-versed in Charles River IMS (CRD)and its functionalities

Excellent analytical and problem-solving skills

Experienced in quality assurance life cycles and testing methodologies

Effective communicator, capable of engaging with both technical and non-technical stakeholders

Detail-oriented, skilled in identifying and correcting data discrepancies and errors

Strong analytical skills for examining data and recognizing patterns or issues

Familiarity with Agile methodology and Atlassian tools is advantageous

Technically proficient with data management tools and software, such as SQL, Excel, and data visualization tools

Knowledge in snowflake

Problem-solving skills to devise solutions for enhancing data quality

Excellent verbal and written communication abilities for collaborating with team members and stakeholders

Self-motivated and proactive in identifying and resolving data quality issues

Strong organizational skills and attention to detail

Ability to work independently and as part of a team

Eager to learn and adapt to new technologies and methodologies