ResponsibilitiesStrategic Technical Leadership: Drive the vision, design, and execution of scalable, high- performance data platforms and solutions.Business Impact & Value Creation: Drive data-driven decision-making, ensuring solutions deliver measurable business impact across the organization.Enterprise Data Architecture: Define and implement data architecture principles, ensuring alignment with Elancos enterprise-wide data strategy.Innovation & Modernization: Lead modernization initiatives to transition legacy data products to modern data architectures, ensuring optimal performance and scalability.Technical Governance: Establish enterprise standards, frameworks, and patterns for data engineering, ensuring alignment with security, compliance, and performance best practices.Hands-on Technical Ownership: Provide architectural guidance and technical mentorship to engineering teams, ensuring best-in-class data product development.Data Pipeline Optimization: Architect and oversee the development of highly efficient, fault-tolerant, and cost-optimized data pipelines across Azure, Databricks, and GCP.Security and Compliance: Partner with security teams to ensure data engineering solutions adhere to security and regulatory standards, implementing governance best practices.Cross-Functional Collaboration: Work closely with Product Owners, Data Architects, and Engineering Squads to deliver robust data solutions in agile sprints.Future-Proofing Data Engineering Capabilities: Continuously evaluate new tools, frameworks, and industry trends to future-proof Elanco’s data engineering landscape.Drive the adoption of AI-driven automation, DataOps, and DevSecOps methodologies Provide architectural leadership in Agile delivery by guiding engineering squads through sprints, backlog refinement, and iterative solution design. Embed a culture of "working out loud" to drive transparency and collaboration.Drive proof-of-concept initiatives, rapid prototyping, and pilot implementations to test and validate new data engineering approaches. Offer hands-on guidance for the development of highly scalable and reliable data products.Serve as the escalation point for complex data engineering challenges, diagnosing issues across ingestion, processing, and storage layers while providing expert-level technical solutions.Collaborate with Data Architects and Engineering Teams to drive consistency in data engineering patterns across multiple domains, enabling a unified data ecosystem with seamless data interoperability.Leverage modern product approaches to influence and shape the business, e.g. discovery, rapid prototyping, and embedding a culture of working out loud.Qualifications:Bachelor’s Degree in Computer Science, Software Engineering, or equivalent professional experience.8+ years of experience engineering and delivering enterprise scale data solutions, with examples in the cloud (especially Databricks, Azure, and GCP) strongly preferred.2+ years in roles requiring technical leadership and/or coaching and development of colleagues.Additional Skills/Preferences:Proven track record in leading and delivering on complex data projects. Expertise in data management, information integration and analytics practices and capabilities. Experience working with modern data architecture and engineering methodologies (Domain driven data architecture, Scalable data pipelines, DataOps (CI/CD), API-Centric Design, SQL/NoSQL, FAIR data principles, etc.) Exposure with developing data pipelines and data products using Azure storage, search, catalog, API management, and data processing & analytics services such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics and PowerBI. Experience working within a “DevSecOps” culture, including modern software development practices, covering Continuous Integration and Continuous Delivery (CI/CD), Test-Driven Development (TDD), etc.Familiarity with machine learning workflows, data quality, and data governance. Experience working in complex, diverse landscapes (business, technology, regulatory, partners, providers, geographies, etc.) Proven track record as a coach and/or mentor in developing technical skills. Good interpersonal and communication skills; proven ability to work effectively within a team. Awareness of Infrastructure automation and application techniques and technologies such as Terraform and Ansible.

Mandatory Skill : Python/ Pandas, SQL, AzureSpark SQL is okay, with basic knowledge of spark core concepts

SQL, Python/ Pandas, Azure, PySpark.PySpark spark SQL is Mandatory, with basic knowledge of spark core concepts