Middle Data & AI Support Engineer

Svitla Systems Inc. is looking for a Middle Data and AI Support Engineer for a full-time position (40 hours per week) in Mexico. Our client is a global, science-led biopharmaceutical company specializing in discovering, developing, and commercializing prescription medicines. The company operates in several therapeutic areas, including oncology, cardiovascular, renal, metabolic, respiratory, immunology, and rare diseases. They work alongside the world's leading academic and biotech research institutions to stimulate innovation and evaluate emerging technologies. The client operates in over 100 countries, and its innovative medicines are used by millions of patients worldwide. The headquarters is located in Cambridge, United Kingdom. This location aligns with the focus on science and innovation, providing proximity to leading academic and research institutions.

The Critical Operations team is responsible for keeping US Market data accurate, reliable, and available to business users across areas such as samples, calls, medications, products, and sales.

As a Data Support Engineer, you will provide production support for data pipelines, monitoring Airflow DAGs, SnapLogic pipelines, and Databricks jobs, investigating data issues, and managing incidents, service requests, and problem records. The role focuses on hands-on data investigation, SQL-based validation, documented recovery procedures, and effective issue escalation within defined SLAs.

Requirements

• 3+ years of experience providing production support for data pipelines or ETL processes, working within SLAs in a shift-based or follow-the-sun support model.
• Hands-on experience investigating data issues end-to-end, from source or vendor files through ingestion, transformation, and loading to published tables or reports.
• Advanced SQL skills for data analysis and reconciliation, including joins, CTEs, window functions, row-count reconciliation, and identifying missing or duplicate loads.
• Production experience with Apache Airflow, including monitoring DAG runs, reading task logs, and interpreting Python tracebacks.
• Hands-on experience with a cloud data platform, preferably Databricks or Snowflake; experience with Amazon Redshift or an equivalent platform is also considered.
• Experience handling incidents, service requests, and problem records within an ITIL-based process and defined SLAs.
• Ability to document investigation findings clearly, identify the appropriate resolver team, and provide evidence to support escalation.
• Ability to work independently within documented recovery procedures and escalate issues that require further technical intervention.
• Working English, with the ability to communicate effectively during daily handovers with the team in India.

Will be a plus

• Experience with Databricks, including job runs, Delta tables, and Databricks SQL.
• Experience with Snowflake and Amazon Redshift.
• Experience with SnapLogic or a similar ETL tool.
• Experience working with AWS S3.
• Proactive use of AI tools for troubleshooting, SQL analysis, and technical documentation.
• Basic Python skills for troubleshooting data pipelines.
• Experience with ServiceNow.
• Pharmaceutical industry experience, ideally involving US commercial data such as IQVIA data, CRM calls, and samples.

Responsibilities

• Monitor Airflow DAG runs, SnapLogic pipelines, and Databricks jobs during the assigned shift.
• Investigate pipeline failures by identifying the failing step in logs, checking source file availability in S3, and validating data freshness using SQL.
• Prioritize incidents based on business impact when multiple pipelines fail simultaneously.
• Execute documented recovery procedures from established runbooks without making code changes.
• Escalate issues that cannot be resolved through documented recovery procedures to the appropriate resolver group, providing the relevant evidence and investigation findings.
• Validate data loads using SQL in Databricks, Amazon Redshift, and Snowflake, including row counts by period, missing periods, and duplicate records.
• Manage incidents, service requests, and problem records in ServiceNow within defined SLAs.
• Keep requesters informed throughout the resolution process and document resolution details according to team standards.
• Provide written handovers to the team in India, including open issues, actions taken, and pending decisions.
• Document new failure patterns in the relevant runbook or knowledge base and flag recurring issues for further investigation.