Lead Data Engineer (AWS)

Location: Hybrid; qualified candidates must live or relocate within a 180-mile radius of Bloomington, IL; Richardson, TX; Tempe, AZ; or Dunwoody, GA, with time split between home and office. Applicants must be eligible to work lawfully in the United States immediately; visa sponsorship is not available.

Company

hirify.global provides insurance and financial services while investing in communities and customer-focused innovation.

What you will do

• Design and build data capabilities supporting experimentation, product development, and emerging technology solutions.
• Write code to extract, transform, process, and move data, and design and maintain platforms for structured, semi-structured, and unstructured data.
• Partner with software engineers, product teams, business partners, data scientists, and ML engineers to define how data is accessed, analyzed, and used.
• Build, deploy, monitor, troubleshoot, and maintain production-quality data solutions.
• At the Lead level, provide technical leadership, mentor engineers, influence architecture and engineering practices, and help shape data capabilities.

Requirements

• 3+ years of Python development for extracting, transforming, processing, and moving data.
• Proficiency writing SQL queries and working with relational databases.
• Experience designing, building, and maintaining ETL/ELT data pipelines.
• Experience working with structured, semi-structured, and unstructured data, including CSV, JSON, and text.
• Experience with public cloud platforms, preferably AWS, including S3, Glue, and Lambda.
• Experience using Git and modern version-control practices, plus familiarity with CI/CD and deploying code to shared or production environments.
• Comfort working in Linux and command-line environments.
• Understanding of testing, maintainability, observability, and reusable software design principles.
• Ability to troubleshoot data quality, pipeline, integration, and performance issues.

Nice to have

• Experience designing data architectures for new products, experiments, or emerging technology use cases.
• Familiarity with machine learning concepts and the end-to-end ML lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, monitoring, and iteration.
• Experience partnering with data scientists or ML engineers to operationalize models and build supporting pipelines and infrastructure.
• Familiarity with generative AI, large language models, and AI-enabled application data considerations.
• Experience designing or building LLM-based and agentic workflows using tools, APIs, retrieval, orchestration, or multi-step reasoning.
• Experience using AI-assisted development tools and critically evaluating AI-generated code and outputs using appropriate engineering, security, testing, and governance practices.
• Experience with APIs, event-driven architectures, streaming data, object storage, modern data platforms, or large and complex datasets.
• Understanding of data governance, security, privacy, lineage, and access management.
• Experience in Agile or product-oriented engineering environments, translating business needs into technical data requirements, working across teams, and managing technical dependencies.
• Comfort experimenting with new technologies and solving problems where the path forward is not fully defined.

Culture & Benefits

• Hybrid work environment combining home and office work.
• Cross-functional collaboration with technical and non-technical partners.
• Work focused on innovation, experimentation, and emerging technology solutions.
• Opportunity to continuously develop skills as technologies and business needs evolve.