Senior Data Engineer

About Ping Identity: At Ping Identity, we believe in making digital experiences both secure and seamless for all users, without compromise. We call this digital freedom. And it's not just something we provide our customers. It's something that inspires our company. People don't come here to join a culture that's built on digital freedom. They come to cultivate it. Our intelligent, cloud identity platform lets people shop, work, bank, and interact wherever and however they want. Without friction. Without fear. While protecting digital identities is at the core of our technology, protecting individual identities is at the core of our culture. We champion every identity. One of our core values, Respect Individuality, reminds us to celebrate differences so you are empowered to bring your authentic self to work. We're headquartered in Denver, Colorado and we have offices and employees around the globe. We serve the largest, most demanding enterprises worldwide, including more than half of the Fortune 100. At Ping Identity, we're changing the way people and businesses think about cybersecurity, digital experiences, and identity and access management. Summary : In this role, you will design and maintain scalable, robust data pipelines, handle large-scale data ingestion, and design workflows using Databricks, PySpark, Python, and SQL, leveraging AWS and other cloud technologies. You will work across the full data lifecycle — from raw data processing to developing, deploying, and monitoring data-driven models, that help enable predictive insights that directly impact business strategy. This is an ideal opportunity for anyone who is passionate about hands-on data engineering, enjoys working across the end-to-end data lifecycle, and thrives in a collaborative, cloud-first environment. This is an ideal role for someone with strong programming and cloud skills, data engineering expertise, and a passion for data science and analytics. Key Responsibilities Data Engineering Pipeline Management Design, implement, and maintain robust ETL/ELT pipelines for ingesting structured and unstructured data from multiple sources. Ensure data quality, consistency, and availability for downstream analytics and modelling. Leverage tools like Databricks for data warehousing. Infrastructure Cloud Management Manage cloud-based data infrastructure (GCP, AWS, or Azure) with an emphasis on scalability, cost optimization, and reliability. Apply Infrastructure as Code (IaC) principles using tools like Terraform or CloudFormation. Data Modelling Feature Engineering Build and maintain logical and physical data models for analytics and ML workloads. Perform feature engineering, data transformations, and statistical preprocessing to prepare datasets for modelling. Machine Learning Advanced Analytics Understanding of machine learning and predictive models using Python (scikit-learn, TensorFlow, PyTorch) or R from a data engineering perspective. Understanding of statistical modelling, time-series forecasting, NLP, or computer vision techniques as needed from a data engineering perspective. Production Monitoring Deploy models into production pipelines and monitor their performance, accuracy, and drift. Ensure models and data pipelines are scalable, maintainable, and automated. Collaboration Mentorship Work closely with data analysts and business stakeholders to define requirements and deliver impactful solutions. Mentor colleagues on best practices in data engineering, and analytics engineering. Skills Qualifications Required: 4–6 years of experience in data engineering, data Science, and statistics roles. Bachelor’s or Master’s degree in Engineering. Strong programming skills in Python, including PySpark for distributed data processing. Advanced SQL expertise and experience designing data models for analytics and ML. Experience in Javascript, HTML, Appscript Solid grounding in software engineering best practices — version control, test ... (truncated, view full listing at source)