Senior Data Engineer

GSK

. Lead the design of production data systems for a team and its domains .

Posted 10/26/2026full-timePhiladelphia • Pennsylvania • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and leading production data systems, with a strong focus on AI-grade infrastructure, data quality governance, and cloud data architecture. Proven ability to mentor teams and drive measurable outcomes in data quality initiatives.

Highest-signal resume keywords

Data Engineering ExperienceAI-Grade Data Infrastructure DesignData Quality Governance StandardsCloud Data Infrastructure DesignInfrastructure As Code

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData Quality StandardsAI Lifecycle ManagementBatch and Streaming ArchitecturesOrchestration FrameworksReal-Time Data FlowsVersioningDrift MonitoringData TransformationDigital-Worker Integration

Soft Skills

MentoringLeadershipCollaborationProblem-SolvingCommunication

Tools & Technologies

DatabricksBigQueryAirflowDbtSpark

Certifications & Qualifications

Bachelor's Degree in Computer ScienceData Engineering

Industry Keywords

Governed Context LayerData Quality InitiativesObservabilityIncident WorkflowsHigh-Stakes Data Environments

Tech Stack

Tools & technologies

AirflowBigQueryCloudSpark

About the role

Key responsibilities & impact

Lead the design of production data systems for a team and its domains
Design pipelines serving multiple downstream consumers and AI-grade infrastructure
Define architectural standards, quality checks and review patterns for digital-worker-generated work
Design digital-worker integration into orchestration, observability and incident workflows
Lead responses to significant data quality incidents through permanent resolution
Set context standards for definitions, contracts, classification and lineage in the governed context layer
Lead contributions to the governed context layer
Define data quality and governance standards and drive adoption of quality frameworks
Lead data quality initiatives with measurable outcomes
Design AI-grade data infrastructure, including evaluation datasets, retrieval architectures and real-time data flows
Lead AI data lifecycle management, including versioning, drift monitoring and governance of AI artefacts
Lead cloud data infrastructure design and infrastructure-as-code adoption
Build platform components used by multiple engineers and digital workers
Improve testing, CI/CD for data, observability, cost management and on-call practices
Set documentation and code quality standards
Mentor junior and mid-level engineers
Partner with FDEs, ML engineers and AI product teams on production data requirements and trade-offs

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience
8+ years of data engineering experience, including significant time leading the design and operation of complex production data systems
Expertise with modern warehouses or lakehouses, orchestration and transformation frameworks, and batch and streaming architectures, such as Databricks, BigQuery, Airflow, dbt or Spark
Experience leading the design of AI-grade data infrastructure for production AI systems, including evaluation datasets, retrieval architectures and real-time data flows
Experience leading data lifecycle management for AI systems, including versioning, drift monitoring and governance of AI artefacts
Track record of defining data quality and governance standards for a team or domain that others adopted, with measurable outcomes
Experience leading cloud data infrastructure design with infrastructure as code
Experience designing and operating AI agents or substantial LLM-powered tools inside data engineering workflows, including tool design, evaluation, observability and human-in-the-loop controls, and AI-assisted development patterns adopted by a team
Preferred: Technical lead for one or more data platforms or significant domains, from first build through to scale
Preferred: Built or improved internal developer platforms or tooling for data engineering
Preferred: Experience with catalogue, metadata, semantic or lineage infrastructure
Preferred: Experience in regulated or high-stakes data environments

Benefits

Comp & perks

Comprehensive benefits program for US employees
Flexible working arrangements
Remote work flexibility
On-site collaboration
Inclusive work environment
Accommodation or other assistance available for applicants