Data Engineer, SQL, ETL, PySpark & Spark

Senior Data Engineer Analyst

Required Education: Bachelor's degree in computer science, Information Systems, Engineering, or equivalent experience

Required Qualifications:

• 5–8 years of experience in data engineering, SQL development, data analysis, or related field
• Strong proficiency in SQL and experience with relational databases and data warehousing concepts
• Experience with ETL tools, data pipeline frameworks, and big data technologies (preferred)
• Knowledge of Quality Engineering (QE) fundamentals, including data validation, testing frameworks, and automation
• Strong problem-solving, analytical, and troubleshooting skills
• Excellent communication and stakeholder management abilities
• Experience working in complex, cross-functional environments

The Senior Data Engineer Analyst is a seasoned technical professional responsible for building, optimizing, and maintaining scalable data pipelines and ensuring data quality across platforms. This role applies in-depth knowledge of data engineering principles, SQL development, and quality engineering (QE) fundamentals to improve data processes, workflows, and system reliability.

• Applies strong expertise in data engineering, contributing to the design and enhancement of data pipelines, architectures, and data quality frameworks.

• Demonstrates a deep understanding of how data systems integrate across functions, enabling efficient data flow and alignment with business objectives.

• Evaluates moderately complex data challenges, balancing performance, scalability, and data integrity considerations using multiple data sources.

• Utilizes advanced SQL skills to design, optimize, and troubleshoot queries, stored procedures, and data transformations.

• Strong analytical, communication, and collaboration skills are required to work across technical and business teams.

• Influences technical decisions through advisory support and contributes to large-scale data initiatives with regional or global impact.

Required Responsibilities:

• Design, build, and maintain scalable data pipelines and ETL processes to support analytics and operational use cases
• Develop and optimize complex SQL queries for data extraction, transformation, and validation
• Implement data quality checks, validation rules, and QE practices to ensure accuracy, consistency, and reliability of data
• Partner with business and technology teams to translate requirements into technical data solutions
• Analyze and troubleshoot data issues, performing root cause analysis and implementing fixes
• Drive data governance and standardization efforts across systems and platforms
• Support system integration and data flow across multiple applications and environments
• Lead data-related aspects of system changes, from requirements through development, testing (QE), and deployment
• Collaborate with QE teams to define test strategies, automate data validation, and ensure end-to-end system quality
• Communicate risks, dependencies, and impacts of data solutions to stakeholders
• Ensure compliance with data governance policies, regulatory requirements, and best practices