Data Engineer (AI)

Location: Hybrid in Frisco, Texas, United States; candidates must live within commuting distance of the Frisco office and work onsite as needed. Relocation assistance is not offered.

Salary: USD $93,370–$153,400 per year, depending on experience and qualifications.

Company

hirify.global develops consumer personal security solutions that protect people, families, and communities in an always-online world.

What you will do

• Design, build, optimize, and maintain scalable data ingestion, transformation, feature, and analytical data pipelines.
• Process and analyze large-scale product telemetry, customer, behavioral, feedback, support, and operational datasets using Python, SQL, Spark, and cloud data technologies.
• Establish practices for data quality, observability, reliability, performance, governance, and trusted analytical datasets.
• Develop features, metrics, cohorts, customer segments, statistical analyses, experiments, and machine learning models for customer and product outcomes.
• Apply regression, classification, clustering, anomaly detection, time-series analysis, forecasting, predictive modeling, NLP, embeddings, LLMs, and Generative AI where appropriate.
• Partner with Engineering and Product teams to turn ambiguous questions into measurable problems, actionable recommendations, production models, monitoring, retraining, and measurable outcomes.

Requirements

• 5+ years of hands-on Data Engineering experience, including building and supporting scalable data pipelines, transformations, and analytical datasets.
• Advanced proficiency in Python and SQL and experience with large-scale structured and unstructured datasets.
• Experience with Apache Spark and modern ETL/ELT frameworks.
• Experience with cloud data platforms and services such as AWS, Azure, or GCP.
• Strong understanding of data modeling, data quality, data transformations, performance optimization, and reliable analytical datasets.
• Strong analytical and problem-solving skills, with interest in progressing from 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 pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Ability to progress from raw data through hypothesis, analysis or model, conclusion, recommendation, and measurable outcome; communicate findings to technical and non-technical stakeholders; and develop deeper Data Science and Machine Learning expertise.

Nice to have

• Exposure to behavioral analytics, anomaly detection, segmentation, forecasting, predictive analytics, or other applied Data Science techniques.
• Familiarity with NLP, LLMs, embeddings, vector databases, RAG, and Generative AI for structured and unstructured data.
• Coursework, certification, specialization, or a degree in Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related discipline.

Culture & Benefits

• Flexible work hours, social programs, and family-friendly benefits.
• Bonus program and 401(k) retirement plan.
• Medical, dental, vision, life, and short- and long-term disability coverage.
• Paid parental leave, 14 paid company holidays, paid time off, sick time, and vacation benefits.
• Diversity, inclusion, support, and community involvement programs.