<p><strong>Position Summary:</strong></p><ul><li>We are looking for a Data Operations Engineer to support and oversee the automated data‑pipeline environment built on AWS. This position bridges data engineering and customer operations, ensuring that incoming datasets are processed accurately, consistently, and securely within established ingestion and transformation frameworks.</li><li>Key responsibilities include monitoring automated workflows, troubleshooting processing failures, validating data quality, and helping onboard new customers by aligning their data formats to a standardized internal model.</li><li>The role requires strong proficiency in SQL and Python, practical experience with AWS services, and the ability to communicate effectively with external customers when data issues arise.</li></ul><p><strong>Responsibilities:</strong></p><p><strong>Data Pipeline Monitoring &amp; Operations:</strong></p><ul><li>Monitor automated batch and streaming data pipelines in AWS</li><li>Identify, troubleshoot, and resolve data processing failures</li><li>Investigate file‑level errors, schema mismatches, and transformation issues</li><li>Perform root‑cause analysis and document resolutions</li><li>Ensure data integrity, completeness, and timeliness across environments</li><li>Escalate architectural or systemic issues to the Data Engineering team</li></ul><p><strong>Customer Data Onboarding &amp; Implementation:</strong></p><ul><li>Collaborate directly with customers to understand their file formats and data structures</li><li>Create and maintain mapping templates to align customer data to a normalized data model</li><li>Validate sample files and run tests on ingestion workflows</li><li>Configure ingestion parameters within predefined frameworks</li><li>Support customer go‑live processes and initial data processing cycles</li></ul><p><strong>Data Quality &amp; Continuous Improvement:</strong></p><ul><li>Write SQL queries to validate data accuracy and research anomalies</li><li>Develop lightweight Python scripts for validation, transformation checks, or automation tasks</li><li>Improve monitoring processes, internal documentation, and operational playbooks</li><li>Work with engineering teams to strengthen platform reliability and observability</li></ul><p><strong>Customer &amp; Cross‑Functional Collaboration:</strong></p><ul><li>Communicate clearly with customers regarding file issues or data discrepancies</li><li>Partner with internal teams including Data Engineering, Product, and Support</li><li>Provide feedback to enhance scalability, resilience, and overall platform performance</li></ul>

<p><strong>Required Qualifications:</strong></p><ul><li>3–5 years of experience in data operations, data engineering, or data support roles</li><li>Experience with ETL/ELT orchestration tools</li><li>Hands‑on experience with AWS services such as S3, Glue, Lambda, CloudWatch, Redshift, or similar</li><li>Strong SQL skills (joins, aggregations, troubleshooting data discrepancies)</li><li>Working knowledge of Python for scripting and data validation</li><li>Experience troubleshooting automated data pipelines</li><li>Familiarity with structured and semi‑structured data formats (CSV, JSON, Parquet)</li><li>Strong analytical and problem‑solving skills</li><li>Comfortable interacting with customers in technical discussions</li><li>Experience working in healthcare or pharmaceutical industry is a must</li></ul><p><strong>Preferred Qualifications:</strong></p><ul><li>Experience working within a data lake or data‑warehouse architecture</li><li>Familiarity with data catalogs or governance frameworks</li><li>Understanding of data normalization and schema design</li><li>Experience working in a SaaS environment</li></ul>

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