Data Engineer

We are looking for a strong Data Engineer with an analytical and Data Science mindset to help transform large-scale product, customer, telemetry, behavioral, and operational data into trusted datasets, actionable insights, and predictive intelligence. This role will initially focus on Data Engineering, including building, optimizing, and maintaining scalable data pipelines, transformations, and analytical datasets. With our core data pipelines and datasets increasingly established, the role is expected to quickly expand into Data Science, statistical analysis, machine learning, and predictive modeling.

This is a Hybrid position based in Frisco, TX. We are only considering candidates within a commutable distance to the Frisco office. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office. We are not offering relocation assistance.

• Design, build, optimize, and maintain scalable data pipelines and analytical datasets supporting analytics, machine learning, and AI use cases.
• Build reliable data ingestion, transformation, and feature pipelines across large-scale product telemetry, customer, behavioral, feedback, and operational datasets.
• Establish strong practices around data quality, observability, reliability, performance, and governance to ensure datasets are trusted and ready for analysis and modeling.
• Use Python, SQL, Spark, and modern cloud data technologies to process, transform, analyze, and derive insights from large-scale datasets.
• Analyze product telemetry, usage, behavioral, customer feedback, support, and operational datasets to identify trends, patterns, anomalies, friction points, and opportunities.
• Develop meaningful features, metrics, cohorts, and customer segments that enable deeper understanding of customer behavior and product performance.
• Progressively develop statistical and machine learning models to understand customer behavior, product usage, engagement, friction, and customer outcomes.
• Apply techniques such as regression, classification, clustering, segmentation, anomaly detection, time-series analysis, forecasting, and predictive modeling based on the problem being solved.
• Develop and test hypotheses using statistical analysis and experimentation to understand the factors driving customer and product outcomes.
• Build predictive approaches that help identify emerging customer issues, behavioral changes, product friction, performance issues, and other signals before they become broader problems.
• Apply NLP, embeddings, LLMs, and Generative AI where appropriate to extract insights from unstructured customer signals such as feedback, surveys, reviews, and support interactions.
• Evaluate analytical and machine learning models using appropriate statistical and ML techniques and ensure conclusions are supported by the underlying data.
• Partner closely with Engineering and Product teams to translate ambiguous customer and product questions into measurable data problems, analytical hypotheses, and actionable recommendations.
• Move beyond dashboards and descriptive reporting by translating data into insights, predictions, recommendations, and measurable product outcomes.
• Develop scalable approaches for operationalizing successful models against production datasets, including model monitoring and retraining where appropriate.
• Measure whether insights and resulting product or engineering changes produce the expected impact, creating a continuous learning loop between data, insights, actions, and customer outcomes.

About You

• 5+ years hands-on experience in Data Engineering, with experience building and supporting scalable data pipelines, transformations, and analytical datasets.
• Advanced proficiency in Python and SQL with experience working with large-scale structured and unstructured datasets.
• Experience with data processing technologies such as Apache Spark and modern ETL/ELT frameworks.
• Experience working with cloud data platforms and services in environments such as AWS, Azure, or GCP.
• Strong understanding of data modeling, data quality, data transformations, performance optimization, and building reliable analytical datasets.
• Strong analytical and problem-solving mindset, with a demonstrated interest in moving beyond data engineering into Data Science, Machine Learning, and advanced analytics.
• Working knowledge of statistics, probability, hypothesis testing, regression, classification, clustering, and predictive modeling.
• Hands-on exposure to…