Senior AI Data Infrastructure Engineer

GlaxoSmithKline; Philadelphia (Philadelphia County); Hybrid; USD 150,000 - 230,000; Full time

Job summary

GlaxoSmithKline seeks a Senior Data Engineer to design production data systems, architect AI-grade infrastructure, and mentor engineers across domains. You will shape pipelines, governance standards, and the integration of digital workers into runtime platforms.

Based in the United States, the role offers a hybrid model with collaboration across teams to ensure data quality, reliability and scalable analytics that support the company's AI initiatives.

Qualifications

• 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.
• A 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 that a team adopted.

Responsibilities

• Lead the design of production data systems for a team and its domains, including pipelines that serve multiple downstream consumers and the AI-grade infrastructure beneath them.
• Define how the team works with digital workers through architectural standards, quality checks and review patterns their work must meet within EAI's operating model.
• Raise the engineering bar for the team by setting the code, documentation and governance standards others use as the baseline, and by mentoring junior and mid-level engineers.
• Lead the design of production pipelines that serve multiple downstream consumers, and define the architectural standard the team and its digital workers apply.
• Define the team's patterns for agent-generated work within the operating model: what digital workers build and remediate on their own, checks required, and when human review is needed.
• Design how digital workers integrate into orchestration, observability and incident workflows with clear tool design, evaluations, guardrails and human-in-the-loop controls.
• Lead the response to significant data quality incidents, from detection to permanent resolution, and drive improvements to pipeline quality.
• Set the context standards for the team's domains: definitions, contracts, classification and lineage captured in the governed context layer.
• Lead the team's contributions to the context layer and feed operational experience into its evolution across EAI.
• Define data quality and governance standards for the team's domains, encode them in the context layer and drive adoption.
• Lead data lifecycle management for AI systems, including versioning, drift monitoring and governance of artefacts; collaborate with ML engineers on dataset design.
• Lead cloud data infrastructure design for the team's domains, promoting IaC and measurable deployment reliability.
• Build platform components used by multiple engineers and digital workers to reduce setup time for new data projects.
• Raise the team's operational bar across testing, CI/CD for data, observability, cost management and on-call practice.
• Set the documentation and code quality standard for the team, and drive adoption of practices used by others.
• Mentor junior and mid-level engineers through pairing, review and direct feedback with measurable improvement.
• Partner with forward deployed engineers, ML engineers and AI product teams on data requirements of production systems.

Skills

Data engineering

Databricks

BigQuery

Airflow

dbt

Spark

Data analysis

Data architecture

Data governance

SQL

ETL development

Data modeling

Education

Bachelor's degree

Tools

Databricks

BigQuery

Airflow

dbt

Spark

Job description

GlaxoSmithKline seeks a Senior Data Engineer to design production data systems, architect AI-grade infrastructure, and mentor engineers across domains. You will shape pipelines, governance standards, and the integration of digital workers into runtime platforms.

Based in the United States, the role offers a hybrid model with collaboration across teams to ensure data quality, reliability and scalable analytics that support the company's AI initiatives.