Sample JD:
Key Responsibilities:
Identify and handle missing, inconsistent, or duplicate data in structured datasets.
Detect and correct anomalies, such as null values, negative entries, and outliers.
Apply logic-based cleaning to resolve inconsistencies, such as date mismatches and incorrect formats.
Standardize data formats, naming conventions, and categorical variables for consistency.
Collaborate with data engineers and an alysts to improve data pipelines and quality checks.
Develop and implement data validation rules to ensure continuous data integrity.
Automate data cleansing processes using scripting tools (Python, SQL, Excel, etc.).
Document data quality issues and propose long-term improvements to data governance.
Work closely with business stakeholders to understand data requirements and improve data usability.
Prepare clean and structured datasets for meaningful aggregation, visualization, and reporting.
Requirements:
Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
4-6 years of experience in data cleansing, validation, or data quality management.
Strong proficiency in SQL for data extraction, validation, and cleaning.
Experience with data analysis and cleaning tools such as Python (pandas, NumPy), Excel, or ETL platforms.
Familiarity with data visualization tools like Power BI, Tableau, or similar.
Understanding of database structures, relational databases, and data warehousing.
Excellent problem-solving skills and attention to detail.
Strong communication skills to collaborate with cross-functional teams.
Good to have work experience in ports and logistics domain

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