Software Engineering Advisor – Data Engineer
SoftwareEngineeringAdvisor–DataEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Software Engineering Advisor – Data Engineer at Software Engineering Advisor – Data Engineer. Skills: Data Engineering, AWS, Databricks, SQL, Python. Design and deliver scalable data pipelines in AWS and Databricks. Proactively monitor, troubleshoot, and resolve data pipeline issues”
What You'll Achieve.
Enable faster, more reliable insights for business teams; Improve trust in data assets; Improve efficiency, reduce errors, and accelerate delivery timelines; Contribute to continuous improvement and faster delivery cycles; Deliver meaningful outcomes
Industry & Context.
Solving complex data problems; problem-solving skills with the ability to troubleshoot complex data issues in production environments
What They're Looking For.
Must Have
5+ years of experience in data engineering, software engineering, or related field, 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, 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
Nice to Have
Experience working with REST APIs and tools such as Postman, Familiarity with BI tools such as Tableau or ThoughtSpot, including troubleshooting data issues tied to Redshift, Experience automating workflows and improving operational efficiency in data environments, Exposure to Agile methodologies and DevOps principles in a production setting, Bachelor’s degree in Computer Science, Engineering, or a related field
What You'll Do.
Design and deliver scalable data pipelines in AWS and Databricks
and resolve data pipeline issues
Triage and resolve incidents based on business impact
Develop ad hoc analyses and reports using SQL and Python
Automate manual processes
How You'll Work.
Team & Collaboration
Partner with business stakeholders to investigate and explain data discrepancies; Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
Communication Scope
Ability to communicate clearly with both technical and non-technical stakeholders
Process & Methodology
Deliver solutions within an Agile (Kanban/DevOps) environment
Full Job Description
Join our Data Platform and Analytics Services (DPaAS) team within Finance IT and help shape the future of enterprise data. In this role, you will design and support modern data platforms in AWS and Databricks, enabling faster, more reliable insights for business teams. If you enjoy solving complex data problems, improving data quality at scale, and building high-impact solutions, this is a great opportunity to make a measurable difference. **Responsibilities** * 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 **Required Qualifications** * 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)
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