Design and deliver scalable data pipelines in AWS and Databricks that drive reliable, production-ready data solutions
Proactively monitor, troubleshoot, and resolve data pipeline issues to ensure consistent system performance and data integrity
Triage and resolve incidents based on business impact, ensuring timely and effective issue resolution
Partner with business stakeholders to investigate and explain data discrepancies, improving trust in data assets
Develop ad hoc analyses and reports using SQL and Python to support critical business decisions
Automate manual processes to improve efficiency, reduce errors, and accelerate delivery timelines
Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
Deliver solutions within an Agile (Kanban/DevOps) environment, contributing to continuous improvement and faster delivery cycles

5+ years of experience in data engineering, software engineering, or related field
Strong hands-on expertise in SQL and Python for data processing and analysis
Proven experience designing and supporting data pipelines in AWS environments (S3, EC2, Glue, Redshift, Aurora)
Experience with Databricks for data engineering and SQL analytics
Solid understanding of data warehousing concepts, dimensional modeling, and relational databases
Experience with workflow orchestration tools such as Airflow (DAGs, scheduling, operators)
Proficiency with version control systems (GitLab, GitHub) and CI/CD best practices
Strong problem-solving skills with the ability to troubleshoot complex data issues in production environments
Ability to communicate clearly with both technical and non-technical stakeholders.

Software Engineering Advisor – Data Engineer

Key skills