Azure Data Engineer

Key Responsibilities / Essential Duties

• 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.

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• 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.

• Demonstrated experience with end-to-end data pipeline development — including data ingestion,

transformation, quality validation, and delivery — aligned with enterprise data architecture

standards and pharmaceutical distribution data models.

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• Experience collaborating 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.

• Demonstrated experience maintaining data management standards and governance processes

through the reporting life cycle, including data quality validation, audit compliance, and error

handling procedures.

• Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.

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.

Knowledge, Skills & Abilities

• Expert-level proficiency in designing and building scalable Extract, Transform, Load (ETL) pipelines to

process large-scale structured and unstructured data primarily using Databricks platform, ensuring

alignment with enterprise data architecture standards and pharmaceutical distribution data models.

• Strong experience with data architecture design and management, including data lakes, data

warehouses, and large-scale processing systems that support advanced analytics capabilities across

the organization.

• Proficiency in Structured Query Language (SQL) for complex query development, database

performance tuning, and data manipulation across relational database management systems

(RDBMS), with the ability to optimize data retrieval processes and troubleshoot pipeline

bottlenecks.

• Hands-on experience with data integration and ETL tools such as Informatica and Alteryx for

designing end-to-end data ingestion, transformation, and orchestration workflows across onpremises and cloud environments.

• Solid understanding of data quality and governance processes, including validation checks, error

handling, quarantine procedures, and data management standards to ensure data accuracy,

consistency, and compliance with applicable regulatory requirements.

• Proficiency in business intelligence reporting tools such as Microsoft Power BI, Tableau, and Qlik

Sense for supporting the delivery of Business Intelligence solutions and creating visual

representations of quantitative and qualitative data to drive business decision-making.

• Experience with pipeline health monitoring, automated alerting, and incident resolution — including

diagnosing and resolving issues involving ETL scripts, database connectivity, and infrastructure

components — to minimize impact on downstream consumers.

• Proficiency in data analysis and synthesis techniques with particular attention to trends and patterns

valuable for diagnostic and predictive analytics efforts, including supply chain performance

indicators across pharmaceutical distribution operations.

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• Strong analytical and problem-solving abilities with a detail-oriented mindset and commitment to

data accuracy, audit compliance, source-to-target mapping integrity, and continuous improvement

throughout the reporting life cycle.

• Excellent communication skills with the ability to convey complex data engineering concepts,

architecture decisions, and technical findings to diverse technical and non-technical stakeholders

across multiple time zones, and to collaborate effectively with data scientists, anal