Lead the development and governance of GSK Medical’s AI Context Layer and associated data products, driving strategy, roadmap, and adoption across global medical teams.
Responsibilities
Own the strategy, roadmap and backlog for the Medical AI Context Layer
Define and deliver a governed semantic layer linking medical objectives, tactics, evidence, content and engagement
Drive adoption of medical data products and the AI Context Layer to reduce time‑to‑insight
Champion the voice of Global Medical Teams and Local Operating Companies through continuous user research
Set success metrics, prove value through proofs of value and track KPIs
Requirements
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics or related discipline
Translate business requirements into appropriate data architectures, data models and analytics solutions, including dashboards and reports that support decision-making
Lead data migration projects or workstreams, from source discovery and profiling through mapping, cleansing, transformation, trial migrations, testing, reconciliation, cutover and post-migration support
Define migration testing and reconciliation approaches, manage data quality issues and defects, document evidence and acceptance criteria, coordinate business validation/sign-off
Develop production‑grade data pipelines and transformations using Python, PySpark and SQL
Design and implement lakehouse or modern data platform architectures, including technologies such as Delta Lake, Azure Data Lake Storage and equivalent cloud‑native services
Help design, build, and scale the firm's enterprise data ecosystem, delivering highly scalable data engineering solutions that enable analytics, reporting, operational efficiency, and AI-driven capabilities across Ares' global business.
Compensation
$210,000 - $230,000
Responsibilities
Define and own the enterprise Data Ingestion & Integration strategy and multi-year roadmap
Establish standards, governance, and best practices for enterprise-wide data movement and integration
Build and lead high-performing engineering teams responsible for ingestion, integration, and data connectivity services
Drive adoption of modern, event-driven, API-first, and cloud-native integration patterns
Partner with product teams to prioritize strategic data initiatives aligned to business growth objectives
Responsible for leading the enterprise data engineering organization responsible for transforming raw data into trusted, consumable, and business-ready data products.
Compensation
$210,000 - $230,000
Responsibilities
Define and execute the enterprise Data Engineering and Data Products strategy
Establish engineering standards, frameworks, and best practices for data transformation and product development
Build and lead high-performing teams of data engineers, platform engineers, and data product developers
Drive the transition from project-based data delivery to product-centric data management
Partner with business and technology leaders to prioritize strategic data products and capabilities
Design, build, test, and deploy automated data pipelines and ETL/ELT processes on Azure and Databricks, develop data models, and serve as technical SME for vendor engineers.
Locations (2)
Hyderabad, Telangana, India
Warsaw, Mazovia, Poland
Compensation
Competitive starting salary and benefits program (private health care, P&G stock, saving plans, sport cards).
Responsibilities
Design, build, test, and deploy automated data pipelines and ETL/ELT processes on Azure and Databricks
Write clean, maintainable, production-ready code in Python and PySpark
Develop, tune, and optimize complex SQL queries
Ensure end-to-end data quality, consistency, and reliability
Act as technical point of contact, SME, and quality gatekeeper for vendor deliverables
Design, build, test, and deploy automated data pipelines and ETL/ELT processes on Azure and Databricks platform, develop data models, and oversee vendor deliverables.
Locations (2)
Hyderabad, Telangana, India
Warsaw, Masovian Voivodeship, Poland
Compensation
Competitive starting salary and benefits program (private health care, P&G stock, saving plans, sport cards).
Responsibilities
Design, build, test, and deploy automated data pipelines and ETL/ELT processes on Azure and Databricks
Write clean, maintainable, production-ready code in Python and PySpark
Develop, tune, and optimize complex SQL queries
Ensure end-to-end data quality, consistency and reliability
Act as technical point of contact and quality gatekeeper for vendor deliverables
Responsible for designing, building, and optimizing enterprise wide data platforms within the Data Warehouse.
Responsibilities
Lead and mentor a team of data engineers, conducting code reviews and ensuring development standards
Support troubleshooting and incident management for data-related issues in production
Collaborate with business stakeholders, data scientists, and other team members to gather requirements and translate them into technical specifications
Lead the design, development and deployment of scalable and high-performance data pipelines using Azure Databricks; ensuring the data integrity, availability, efficient extraction, transformation, and loading of data from various sources into the Azure Databricks Data Warehouse
Collaborate with data scientists, analysts, and other engineering teams to deliver business-critical insights. Optimize pipeline performance, cost, and scalability in the Azure cloud environment
Requirements
Bachelor's degree in Computer Science, Engineering, or a related field
Nice to have: Microsoft Certified: Azure Data Engineer Associate (DP-203)
Nice to have: Databricks Certified Data Engineer Professional
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