Senior AWS Data Engineer (Huge Databricks Exp)--W2 Position--Remote--100% Interview

Role: Senior Data Engineer Huge Databricks Experience*

Location - REMOTE

Job Summary:
We are seeking a highly experienced Senior Data Engineer with 12+ years of hands-on experience in data engineering and data pipeline development. The ideal candidate will have strong expertise in Databricks, DBT, AWS, and Apache Spark, and a proven track record of working with modern data platforms in large-scale enterprise environments.

This role requires a professional who is adept at building scalable data pipelines, transforming data using DBT, working extensively with AWS cloud infrastructure, and developing in a Spark/Databricks environment. Strong SQL query writing skills are essential.

Mandatory Skills & Experience:

• 12+ years of experience in data engineering or relevant fields.
• Expert-level knowledge of Databricks including notebooks, jobs, Delta Lake, and workspace management.
• Strong hands-on experience with DBT (Data Build Tool) including writing models, tests, and documentation.
• Proficiency in AWS services including S3, Glue, Redshift, Lambda, IAM, etc.
• Strong experience with Apache Spark both batch and streaming data processing.
• Strong experience writing efficient SQL queries for data transformation and analysis.
• Proven ability to work independently in a remote environment.
• Solid understanding of data modeling, ETL/ELT processes, and modern data architecture.
• Experience in version control (Git), CI/CD pipelines, and Agile methodologies.

Preferred Qualifications:

• AWS Certification (e.g., AWS Data Analytics, AWS Solutions Architect)
• Experience with orchestration tools like Airflow or AWS Step Functions.
• Familiarity with DevOps practices in a data environment.
• Exposure to data quality frameworks and testing strategies.

Responsibilities:

• Design, build, and optimize robust and scalable data pipelines using Databricks and DBT on AWS.
• Develop, deploy, and maintain ELT/ETL processes to transform raw data into actionable insights.
• Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and deliver data solutions.
• Ensure data integrity, quality, and governance across all data flows.
• Continuously monitor and improve performance, reliability, and cost-efficiency of data pipelines.
• Write and maintain high-quality documentation.