Sr. Production Support Engineer

Incident Debugging and Resolution:

Investigate and resolve daily production issues, minimizing downtime and ensuring stability.

Perform root cause analysis and implement solutions to prevent recurring issues.

Data Analysis and Query Writing:

Write and optimize custom queries for MySQL , Postgres , MongoDB , Redshift , or other data systems to debug processes and verify data integrity.

Analyze system and application logs to identify bottlenecks or failures.

Scripting and Automation:

Develop and maintain custom Python scripts for data exports, data transformation, and debugging.

Create automated solutions to address inefficiencies in broken processes.

Process Improvement:

Collaborate with engineering and operations teams to enhance system performance and reliability.

Proactively identify and implement process improvements to optimize workflows.

Collaboration:

Act as the first point of contact for production issues, working closely with developers, QA teams, and other stakeholders.

Document findings, resolutions, and best practices to build a knowledge base.

Required Skills and Qualifications:

Experience: 3-5 years of hands-on experience in debugging, Python scripting , and production support in a technical environment.

Technical Proficiency:

Strong experience with Python for scripting and automation with Pandas .

Proficient in writing and optimizing queries for MySQL , Postgres , MongoDB , Redshift , or similar databases.

Familiarity with ETL pipelines, APIs, or data integration tools is a plus.

Problem-Solving: Exceptional analytical and troubleshooting skills to quickly diagnose and resolve production issues.

Process Improvement: Ability to identify inefficiencies and implement practical solutions to enhance system reliability and workflows.

Communication: Excellent verbal and written communication skills for cross-functional collaboration and documentation.

Nice-to-Have Skills:

Exposure to tools like Airflow , Pandas , or NumPy for data manipulation and debugging.

Familiarity with production monitoring tools like New Relic or Datadog .

Experience with cloud platforms such as AWS, GCP, or Azure.

Basic knowledge of CI/CD pipelines for deployment support.