Job Description
The Data Engineer is responsible for designing, building, and maintaining scalable data infrastructure and pipelines that enable advanced analytics and business intelligence capabilities across the enterprise. This role combines deep technical expertise in ETL pipeline development, data architecture management, and database performance optimization to build production-grade data systems - including data lakes, data warehouses, large-scale processing frameworks, and data quality governance processes aligned with pharmaceutical distribution data models. The Data Engineer collaborates closely with data scientists, analysts, software engineers, and operations teams to onboard new data sources, support system migrations, and deliver reliable, high-performance data services to internal and external customers. The ideal candidate is a hands-on engineer who thrives at the intersection of data architecture and operational excellence, bringing scalable pipeline designs from prototype to enterprise deployment while ensuring data accuracy, consistency, and compliance with applicable regulatory requirements.
Key Responsibilities / Essential Duties
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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.
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Develop and manage data architectures, including data lakes, data warehouses, and large-scale processing systems that support advanced analytics capabilities across the organization.
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Optimize data retrieval processes, troubleshoot pipeline bottlenecks, and fine-tune database performance to maximize efficiency and reduce processing costs.
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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.
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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.
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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.
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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.
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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.
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Maintain data management standards and processes to effectively govern data through the reporting life cycle, proactively identifying opportunities for continuous improvement and operational excellence.
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Validate data for accuracy, process audit reports, propose solutions to remedy deficiencies, and confirm results in accordance with applicable data compliance requirements.
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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.
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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
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Bachelor s degree in Statistics, Computer Science, Information Technology, or equivalent related experience (required).
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Master s degree in Computer Science, Data Engineering, Information Systems, or a related technical discipline (preferred).
Experience
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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.
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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.
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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.
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Demonstrated experience maintaining data management standards and governance processes through the reporting life cycle, including data quality validation, audit compliance, and error handling procedures.
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Experience in a regulated industry (pharmaceutical, healthcare, or life sciences) is a plus.
Certifications (Required / Preferred)
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Microsoft Certified: Azure Data Engineer Associate (DP-203) - Required.
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Databricks Certified Data Engineer Associate - Required.
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Microsoft Certified: Power BI Data Analyst Associate - Preferred.
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Snowflake SnowPro Core Certification - Preferred.
Knowledge, Skills & Abilities
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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.
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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.
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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.
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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 on-premises and cloud environments.
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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.
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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.
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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.
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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.
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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, analysts, software engineers, and operations teams.
Work Environment / Physical Requirements
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CBS - Pune office-based or Pune city-based for remote/hybrid work environment with access to necessary compute resources.
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Standard working hours with flexibility to accommodate project deadlines and team members across multiple time zones.
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Ability to sit for extended periods and use a computer for the majority of the workday.
Competencies
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Technical Excellence: Maintains exceptionally high standards for data pipeline quality, ETL code reliability, and data architecture best practices, ensuring consistent performance, accuracy, and scalability across all engineered data solutions.
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Collaboration & Teamwork: Works effectively with cross-functional teams including data scientists, analysts, software engineers, and operations teams to onboard new data sources, execute system migrations, and deliver integrated data solutions that meet enterprise requirements.
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Adaptability: Thrives in a fast-paced environment where data technologies, pipeline architectures, and business requirements evolve rapidly, adjusting engineering approaches to accommodate new data sources, platforms, and integration patterns.
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Innovation & Continuous Improvement: Actively explores emerging data engineering technologies, tools, and frameworks to optimize pipeline performance, reduce processing costs, and proactively identify opportunities for operational excellence across the data ecosystem.
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Data Quality & Governance Mindset: Champions data accuracy, consistency, and compliance by implementing robust validation checks, error handling, and quarantine procedures aligned with applicable regulatory requirements and enterprise data management standards.
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Results Orientation: Focuses on delivering production-ready, scalable data pipelines and architectures that generate tangible business value, enabling reliable analytics capabilities and measurable improvements in data accessibility and processing efficiency across the organization.
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