Solution Engineer, Data Engineering (Manager)

About this role

Pfizer’s mission to deliver breakthroughs that change patients’ lives is rooted in our commitment to science and innovation. Within Discovery, Preclinical, and Translational Solutions (DP&TS), we work to shorten the path from target identification to clinical translation with better software, data, and AI.

We are building a modern, cloud-native data engineering capability that delivers trusted, reusable, and compliant data products at scale. As a Solution Engineer, Data Engineering, you will work hands-on across ingestion, transformation, and delivery, helping scientific and digital teams turn complex data needs into reliable, well-governed datasets and pipelines.

You will report to the Principal Engineer, Data Engineering, and work day to day with platform engineers, domain teams, data scientists, and product partners. The team values ownership and practical engineering, and the solutions you ship are expected to be secure by design, observable, and ready for production in regulated environments.

What Your Typical Day Will Look Like

Your day may include pairing with a domain team to define a new dataset or data product, implementing or refining pipelines (batch and/or streaming), reviewing pull requests, improving data quality checks and observability dashboards, and troubleshooting a production issue with clear runbooks and root-cause follow-up. You’ll ship changes through CI/CD, document what you build, and continuously improve patterns and tooling so others can move faster with less friction.

Key Responsibilities

• Develop and maintain cloud-native data pipelines for batch and streaming ingestion, ensuring reliability, scalability, and maintainability • Develop transformation and modeling patterns (e.g., dimensional, wide, and domain-oriented models) that enable analytics and AI/ML use cases • Build and operate orchestration workflows with strong operational discipline (versioning, testing, promotion, rollback, and runbooks) • Embed data quality, lineage, and observability into pipelines and datasets (automated checks, monitoring/alerting, and incident response practices) • Implement secure data access patterns (IAM/RBAC, secrets management, encryption, auditing) aligned to enterprise and regulatory requirements • Optimize performance and cost across compute and storage through tuning, partitioning/layout strategies, and transparent cost visibility • Collaborate with stakeholders (scientists, analysts, data scientists, and engineers) to translate requirements into production solutions and iterate based on feedback • Contribute to reusable standards, templates, and self-service enablement (documentation, examples, onboarding materials) to scale adoption • Stay current with new technologies and best practices, proactively seeking opportunities for skill development and knowledge sharing

BASIC QUALIFICATIONS  

Education: Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, Data Engineering, Bioinformatics, Engineering, or related discipline)

• 4+ years of experience in data engineering, software engineering, or closely related roles • Strong proficiency in Python and SQL for data processing, automation, and testing • Hands-on experience with cloud platforms (AWS, GCP, Azure) and cloud-native storage/compute services • Experience with one or more modern data processing or lakehouse/warehouse technologies (e.g., Spark, Databricks, Snowflake, Redshift, Delta Lake, Iceberg) • Experience with workflow orchestration and scheduling (e.g., Airflow) and CI/CD using Git-based workflows • Strong understanding of data modeling, data integration patterns, and software engineering best practices (code review, testing, documentation) • Working knowledge of security best practices for data platforms (IAM, secrets management, encryption, least privilege) and operating in regulated environments • Strong problem-solving skills and eagerness to learn in a collaborative environment • Fluent in English; capable of clear technical communication across scientific and engineering disciplines

PREFERRED QUALIFICATIONS • Experience building streaming or event-driven pipelines (e.g., Kafka, Kinesis, Event Hubs) • Experience with transformation frameworks and automated data quality tooling • Experience with observability practices for data systems (metrics/logs/traces; OpenTelemetry, Prometheus/Grafana, ELK) • Experience with Infrastructure-as-Code and platform tooling (Terraform, Helm, Kubernetes) to enable reproducible environments • Experience working in regulated/compliance frameworks (e.g., GxP, SOC2, HIPAA) and implementing audit-ready delivery practices • Experience partnering with product and domain teams to deliver ‘data-as-a-product’ with clear ownership, documentation, and SLAs/SLOs • Experience with and strong understanding of the software development lifecycle • Experience working in team-based environments, either professionally or academically

  Work Location Assignment: Hybrid

Last Date to Apply for Job: 9/2/2026 
The annual base salary for this position ranges from $99,200.00 to $165,400.00.* In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 12.5% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range prov

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