Senior Data Platform Engineer

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DigiCert

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2003

🏥 Healthcare

📦 Logistics

💼 Consulting

Healthcare • Logistics • Consulting

DigiCert is a global leader in providing high-assurance digital security solutions. It offers a wide range of services including TLS/SSL certificates, PKI (Public Key Infrastructure), and IoT (Internet of Things) security solutions. DigiCert's suite of products and services includes certificate lifecycle management, secure signing for code and documents, and devices across various industries such as healthcare, transportation, and smart cities. The company is focused on ensuring digital trust with advanced encryption and identity verification technologies, preparing organizations for the challenges of the quantum era. DigiCert is trusted by the majority of the Global 2000 companies for managing their digital trust needs.

📋 Description

• Design, build, and maintain scalable data and ML pipelines using Python and SQL across batch and streaming workloads • Own and evolve Databricks platform infrastructure, including Delta Lake architecture, Unity Catalog governance, Databricks Workflows orchestration, and compute optimization • Build and maintain end-to-end ML pipelines covering feature engineering, model training, experiment tracking, and model deployment/serving • Collaborate with data scientists to operationalize models in production-grade ML systems • Define and enforce data platform standards, ingestion patterns, data modeling conventions, medallion architecture, and reliability practices • Implement data quality, observability, and monitoring frameworks • Optimize pipelines for performance, cost, and reliability using Spark and PySpark • Evaluate, integrate, and govern platform tooling and data sources in the Databricks ecosystem • Contribute to architectural decisions and the long-term data platform roadmap • Participate in code reviews, technical design discussions, and engineering standards • Mentor junior engineers and improve platform and data engineering practices • Document platform architecture, pipeline design, and operational runbooks

🎯 Requirements

• 6+ years of experience in data engineering, data platform, or ML engineering roles • Strong proficiency in Python and SQL with production-grade data pipeline experience • Hands-on expertise with Databricks, Delta Lake, Unity Catalog, Databricks Workflows, and PySpark • Experience building and maintaining production ML pipelines, including feature engineering, training, experiment tracking, and model deployment • Familiarity with MLflow or comparable experiment tracking and model registry tools • Experience with cloud data platforms such as AWS, Azure, or GCP • Strong understanding of data modeling, dimensional design, and analytics-friendly data architecture • Experience with batch and incremental/CDC pipeline patterns • Proficiency with Git, version control, and CI/CD practices for data and ML workflows • Strong engineering judgment focused on reliability, maintainability, and cost • Clear communication and comfort working with technical and non-technical stakeholders • Nice-to-have: streaming or near real-time pipelines, feature stores, LLM/RAG or AI/BI tooling, data quality and observability tools, dbt, infrastructure as code, Agile/Scrum, and mentoring or platform standards experience

🏖️ Benefits

• Generous time off policies • Top shelf benefits • Education, wellness and lifestyle support