Data Quality Engineer

Work Mode: Hybrid(3days WFO & 2 days WFH)

Key Responsibilities:

Defining and implementing data quality standards:

Collaborating with stakeholders to establish clear data quality rules and metrics, such as accuracy, completeness, consistency, and timeliness.

Data profiling and analysis:

Examining data to understand its structure, identify anomalies, and assess its quality against defined standards.

Developing and implementing data quality checks:

Creating automated processes and scripts to monitor data quality, detect errors, and trigger alerts when thresholds are breached.

Troubleshooting and resolving data quality issues:

Investigating data errors, identifying root causes, and working with relevant teams to implement corrective actions.

Data validation and testing:

Designing and executing test cases to ensure data accuracy and consistency throughout various data pipelines and systems.

Data quality automation:

Developing and maintaining automated tools and scripts to streamline data quality processes and improve efficiency.

Collaboration and communication:

Working with data engineers, business analysts, and other stakeholders to ensure data quality requirements are met and data quality issues are addressed effectively.

Data governance:

Participating in the development and implementation of data governance policies and procedures to ensure data quality and compliance.

Required Skills and Qualifications:

• Strong understanding of data quality principles and methodologies.
• Proficiency in SQL and relational databases.
• Experience with data quality tools and frameworks.
• Experience with data profiling, validation, and testing techniques.
• Familiarity with data warehousing and cloud platforms (AWS, Azure, GCP) is a plus.
• Programming skills in languages like Python or R.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration skills.

• Need 1 month or less candidates

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