002AVM - Data Engineer

Name of the position: Data Engineer

Location: Remote

No. of resources needed : 01

Mode: Contract (3 Months with Possible Extension)

Years of experience: 4+ Years 

Shift : UK shift 

 

Job Summary:

We are looking for a highly motivated and detail-oriented Data Engineer with a strong background in data cleansing, Python scripting, and SQL to join our team. The ideal candidate will play a critical role in ensuring data quality, transforming raw datasets into actionable insights, and supporting data-driven decision-making across the organization.

Key Responsibilities:

• Design and implement efficient data cleansing routines to remove duplicates, correct anomalies, and validate data integrity.
• Write robust Python scripts to automate data processing, transformation, and integration tasks.
• Develop and optimize SQL queries for data extraction, aggregation, and reporting.
• Work closely with data analysts, business stakeholders, and engineering teams to understand data requirements and deliver clean, structured datasets.
• Build and maintain data pipelines that support large-scale data processing.
• Monitor data workflows and troubleshoot issues to ensure accuracy and reliability.
• Contribute to documentation of data sources, transformations, and cleansing logic.

Requirements:

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field.
• 3+ years of hands-on experience in data engineering, with a focus on data quality and cleansing.
• Strong proficiency in Python, including libraries like Pandas and NumPy.
• Expert-level knowledge of SQL and working with relational databases (e.g., PostgreSQL, MySQL, SQL Server).
• Familiarity with data profiling tools and techniques.
• Excellent problem-solving skills and attention to detail.
• Good communication and documentation skills.

Preferred Qualifications:

• Experience with cloud platforms (AWS, Azure, GCP) and data services (e.g., S3, BigQuery, Redshift).
• Knowledge of ETL tools like Apache Airflow, Talend, or similar.
• Exposure to data governance and data cataloging practices.