Senior Data Engineer

Role & responsibilities

• Design and build scalable Extract, Transform, Load (ETL) pipelines to process large-scale structured and unstructured data, ensuring alignment with enterprise data architecture standards and pharmaceutical distribution data models.
• Develop and manage data architectures, including data lakes, data warehouses, and large-scale processing systems that support advanced analytics capabilities across the organization.
• Optimize data retrieval processes, troubleshoot pipeline bottlenecks, and fine-tune database performance to maximize efficiency and reduce processing costs.
• Interpret data sets with particular attention to trends and patterns valuable for diagnostic and

predictive analytics efforts, creating visual representations of quantitative and qualitative data to support business decision-making.

• Implement data quality and governance processes, including validation checks, error handling, and quarantine procedures to ensure data accuracy, consistency, and compliance with applicable regulatory requirements.

• Support the delivery of Business Intelligence solutions to internal and external customers, utilizing business intelligence tools across departments and functional areas to provide detailed reporting and analysis.

• Assess analytics and reporting needs, provide recommendations for enhanced solutions and specifications for business intelligence tools, and prioritize reporting requirements across process areas to align with deployment strategies.

• Monitor pipeline health, system integrity, and automated alerts proactively, diagnosing and resolving incidents involving Extract, Transform, Load scripts, database connectivity, or infrastructure issues to minimize impact on downstream consumers.

• Collaborate with data scientists, analysts, software engineers, and operations teams on system upgrades, migrations, and integration projects, onboarding new data sources from various applications and interfaces into central repositories.

• Maintain data management standards and processes to effectively govern data through the reporting life cycle, proactively identifying opportunities for continuous improvement and operational excellence.

• Validate data for accuracy, process audit reports, propose solutions to remedy deficiencies, and confirm results in accordance with applicable data compliance requirements.

• Communicate data engineering requirements, architecture decisions, and technical findings to cross-functional partners across multiple time zones, providing expertise and guidance on data infrastructure and pipeline design.

• Document processes, standard operating procedures, source-to-target mappings, and knowledge artifacts to ensure operational continuity during transitions, enabling seamless integration of evolving team structures.

Qualifications

Education

• Bachelors degree in Statistics, Computer Science, Information Technology, or equivalent related experience (required).
• Masters degree in Computer Science, Data Engineering, Information Systems, or a related technical discipline (preferred).

Experience

• Minimum professional experience as per the Level in data engineering, with a focus on designing, building, and maintaining scalable data pipelines and data architectures in enterprise environments.
• Minimum of hands-on experience as per the Level developing and deploying Extract, Transform,

Load (ETL) pipelines, data lakes, data warehouses, and large-scale data processing systems in production environments.

Certifications (Required / Preferred)

• Microsoft Certified: Azure Data Engineer Associate (DP-203) Required.
• Databricks Certified Data Engineer Associate — Required.
• Microsoft Certified: Power BI Data Analyst Associate — Preferred.
• Snowflake SnowPro Core Certification — Preferred.