Principal Data Engineer

Responsibilities:

Engineering

Articulate our technical direction and upskill colleagues(both internal and external) in leveraging our Enterprise Data Platforms to deliver data products and ensure value is well understood.

Provide feedback to our Data Platform Engineering Team to improve their services.

Drive opportunities from the Data Engineering backlog and provide technical leadership in design and execution of those solutions.

Drive Elanco’s Data Engineering standards, leveraging standard languages, frameworksand continually reviewing as appropriate to ensure the correct balance of modern and pragmatic.

Drive a continual modernisation plan to bring legacy data products in line with new standards where appropriate.

Partner with core engineering group to ensure application security is appropriately considered, monitored, and acted upon.

Act as anescalation point of contact to diagnose and problem solve across differentdata products.

Look for opportunities to modernise specific aspects of our data landscape,helping Elanco to maximise investments and drive more reliable outcomes.

Product Delivery

Hands on development, build, governance and maintenanceof our R & D Data Environment.

Partner with the R & D Data Architect to understand theirdata strategy and lead its implementation.

Establish clear ways of working, partnering with Product Managers and Product Owners to ensure reliable and consistent delivery – speaking up when improvements are necessary.

Support, coach and guide other data engineers in R & D.

Drive strong technical standards, technical processes,governance and control.

Lead squadsthrough sprints, building against defined backlog items.

Innovation

Look for opportunities to partner internally and externally using formats to engage, learn and achieve great outcomes for Elanco IT.

Participate in the Data Engineering community across Elanco to inspire, engage and ignite innovation.

Use modern product approaches to influence and shape the business through partnership with Product Management, utilising modern product approaches such as rapid prototyping and embedding a ‘show them, don’t tell them’ culture.

Basic Qualifications: 

Bachelor’s Degree in Information Technology or equivalent professional experience.

15+ years of experience engineering and delivering enterprise scale data solutions, with examples in the cloud (especially Azure, GCP) strongly preferred.

8+ years in roles requiring technical leadership and/or coaching and development of colleagues.

Additional Skills/Preferences: 

Life science / R & D background strongly preferred.

Proven track record in leading and delivering on complex data projects.

First class critical thinking skills, reducing the load on architects.

Expertise in data management, information integration and analytics practices and capabilities.

Experience leveraging Data Contracts through data ingestion and data processing pipelines.

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 Azurecapabilities such as storage, search, catalog, API management, and data processing & analytics services such as Azure Data Factory, Azure Data Bricks, Azure Synapse Analytics and PowerBI.

Exposure with developing on Google Cloud Storage, Google BigQuery, open data formats such as Delta, Apache Hudi, Apache Iceberg, use of Unity Catalog, writing OOP Python code in the form of Classes/Methods, building and maintaining custom python packages, use of Docker images/containers and managing Postgres databases.

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 to various application DB platforms (Azure based, GCP based, Oracle, etc)

Experience using GitHub for source control and GitHub workflows for CI/CD processes.

Experience with unstructured data sets such as image and videos, or exposure to R&D specific file formats like .mol, .sdf, .smi, .fasta, etc… will stand out.

Familiarity with machine learning workflows.

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

Other Information:Occasional travel may be required.

Direct Reports:0