About this role
. Data Architecture & Engineering
- Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
- Design, build, and maintain data pipelines from data sources through integration to consumption for specific use cases.
- Implement robust data modeling standards across bronze, silver, and gold layers in the data lake.
- Develop data models (conceptual, logical, and/or physical) as required.
- Optimize Spark and SQL workloads for performance, scalability, and cost efficiency.
- Manage metadata using data preparation, integration, and AI-enabled tools and techniques.
. Data Integration & Automation
- Drive automation in data integration; recommend and lead implementation of techniques to automate repeatable data preparation and integration tasks.
- Build API-based integrations (REST/JSON) and real-time ingestion frameworks.
- Automate data workflows using Azure DevOps pipelines and Git-based CI/CD practices.
•
Implement parameterized, reusable pipeline templates for ingestion and transformation.
- Develop automated unit, regression, and integration testing frameworks for data jobs.
. Analytics & Data Enablement
- Prepare and curate high-quality datasets for BI, reporting, and advanced analytics.
- Partner with analytics teams using Power BI, Tableau, or similar platforms to define semantic models and KPIs.
- Implement performance-optimized data models for self-service analytics.
- Will occasionally provide support to end users on the use of data visualization solutions.
Stakeholder Engagement & Leadership
- Lead technical design reviews, mentor junior engineers, and promote best practices.
- Assist cross-functional groups, business analysts, and stakeholders to gather, define, and refine data requirements.
- Collaborate with business and IT stakeholders to align data engineering with organizational objectives.
- Propose innovative data ingestion, preparation, and integration techniques to address stakeholder requirements.
- Contribute to architectural roadmaps and technology evaluations for the data platform.
- In collaboration with functional leaders, identify inefficiencies and recommend improvements to the executive team.
Job Experience & Education Requirements:
Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)
And
5–8 years of experience designing and developing enterprise-scale data solutions (data warehouses, data lakes, operational databases)
Other:
- Expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and Azure DevOps.
- Proven experience with Azure Data Factory, ADLS Gen2, and Azure SQL Server.
- Strong experience with Microsoft Azure data management architectures including Data Warehouse, Data Lake, and Data Catalogue, and supporting processes such as Data Integration, Governance, and Metadata Management.
- Experience with Power BI required; Tableau or Looker a plus.
- Working knowledge of CI/CD automation, version control (Git), and infrastructure as code (ARM, Bicep, or Terraform).
- Experience in life sciences or healthcare industries is a strong plus.
- Good understanding of GxP, GDPR/HIPAA, and applicable CFR/CTR/CTD regulations.
- Demonstrated success working with both IT and business stakeholders while integrating analytics and data science output into business processes and workflows.
- Must have excellent written and verbal communication skills.
- Proven ability to work independently and as part of a team and meet important deadlines.
- Statistical analysis skills are an asset.
Commensurate with experience, and comprised of a base salary + Variable Target Incentive %
Tech stack
AzureDatabricksSparkTableauLookerTerraform