Job Summary
Ensure smooth data operations and continuity of data processing pipelines. The role combines technical monitoring, triage, incident recovery, and direct collaboration with data consumers. It requires both analytical and operational mindset owing the issue from detection to resolution.
Key responsibilities
- Monitor daily data processing status through email alerts, scheduling tools (Autosys, UC4), or internal ETL interfaces – t0 detect anomalies or signs of potential issues and taking a proper action for resolving the issues
- Investigate processing errors by following SOPs, analyzing Unix logs, and reviewing Snowflake query history.
- Troubleshoot and recover data pipeline incidents, coordinating with development and operations teams when needed.
- Communicate delays, root causes, and progress transparently through Jira and internal channels.
- Perform data quality checks and resolve detected anomalies or escalate to the correct data owners.
- Support end users with data-related questions, incidents, or change requests, ensuring clear documentation.
- Maintain up-to-date knowledge base entries and contribute to process improvement.
Required skills
Minimal
- Very good communication skills (verbal and written)
- Very good command of English (we work with global clients)
- 2+ year experience in data engineer role
- 2+ year experience with customer support in data related projects
- Strong analytical thinking and problem-solving skills with proactive approach
- Familiarity with below technologies to perform root cause analysis:
- Proven experience with SQL databases
- Proven experience with DWH, Snowflake
- Proven experience with core repository version control tools (Git, BitBucket)
- Jira, Confluence, MS Excel,
- Ability to work within a global support environment
- Handover to polish L2 support team at the end of local shift
Nice to have
- Proven experience supporting data products from both technical and data perspectives
- Experience with ETL monitoring tools (Autosys, UC4, or equivalent)
- Service-oriented attitude, patience, thoroughness and good attention to detail
- Familiarity with data quality tools or frameworks
