The Senior Data Engineer is responsible for leadership and expertise in developing, maintaining, and optimizing data pipelines and infrastructure to ensure the availability, accuracy, and reliability of data for various business functions. This role extends beyond foundational engineering tasks to include leadership, strategic architecture contributions, and advanced integration of cloud and AI/ML technologies. The Senior Data Engineer ensures high-quality data availability for analytics, reporting, and machine learning initiatives across the organization.

REQUIREMENTS:

• A minimum of 7 years of experience in data engineering, with a strong focus on cloud environments is required.
• Experience with AWS is highly preferred.
• Exceptional SQL skills, including experience with SSIS, stored procedures, and data modeling.
• 3+ years of experience working with AI/ML technologies, including model deployment and integration.
• 3+ years of experience with data lake and data warehouse architectures.
• Proficiency in Python, Spark, and other modern data processing frameworks.
• Proven experience in building and automating data pipelines and ETL/ELT processes.
• Strong understanding of data quality, validation, and performance optimization techniques.
• Familiarity with cloud data platforms such as Snowflake, Redshift, or Databricks.
• Excellent communication and collaboration skills to work effectively with cross-functional teams.
• Attention to detail and a commitment to ensuring data quality and accuracy.

• Design, build, and maintain scalable and efficient data pipelines across structured and unstructured data sources.
• Develop and optimize complex SQL queries, stored procedures, and SSIS packages to support data transformation and integration.
• Collaborate with data architects and analysts to design robust data models and schemas for analytics and reporting
• Integrate data from diverse sources (databases, APIs, streaming platforms) into unified, high-quality datasets.
• Automate data workflows and implement monitoring, validation, and testing processes to ensure data integrity and reliability.
• Support the deployment and operationalization of AI/ML models in collaboration with data scientists.
• Optimize data storage and retrieval mechanisms using cloud-native tools and frameworks.
• Identify opportunities for data-related process improvements and work with cross-functional teams to implement them.
• Document data engineering processes, architecture, and best practices.
• Evaluate and recommend new tools and technologies to improve the efficiency and effectiveness of data engineering.
• Mentor data engineers and contribute to team knowledge sharing and continuous improvement.
• Stay updated with emerging trends and technologies in data engineering and adopt best practices to continuously improve the data infrastructure.