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- Advanced Technical Expertise: 5+ years of professional experience in software engineering, data science, or analytics; 3+ years in an architectural or senior technical leadership role with proven ability to design and deliver complex systems
- Data & Analytics Mastery: Deep understanding of machine learning, statistical modeling, data engineering, and decision science; hands-on experience building data pipelines, ETL processes, and analytical models in production environments
- Software Architecture Knowledge: Proven expertise designing scalable, maintainable software systems; strong understanding of cloud platforms (AWS, Azure, GCP), APIs, integration patterns, and deployment architectures
- Programming & Technical Depth: Hands-on proficiency with Python, Java, Scala, or similar languages; ability to review code, guide developers, and solve complex technical problems; familiarity with DevOps, containerization, and CI/CD practices
- Business Acumen & Leadership: Understanding of business strategy and operations; proven ability to engage with executives and business leaders; experience leading technical teams, mentoring others, and driving organizational change
- Problem-Solving & Communication: Exceptional analytical and creative thinking; ability to navigate ambiguity and complexity; clear written and verbal communication skills; comfort translating between business and technical domains
- Demonstrated Impact: Portfolio of completed projects showing end-to-end ownership from design through deployment; evidence of delivering solutions that drove measurable business value and operational improvements
Primary Focus: Data Excellence Over Platform Specificity
While familiarity with Aera Technology is a nice-to-have, data engineering and analytics expertise is what matters most. We value professionals who:
- Prioritize data quality, pipeline reliability, and analytical rigor above platform-specific knowledge
• Can quickly learn new tools and platforms as business needs evolve
• Understand when to build custom solutions vs. leverage existing platforms
• Bring deep experience with core data technologies: Python, SQL, cloud data warehouses, BI tools, and ML frameworks
Cloud Platforms: AWS, Azure, GCP
Data Warehousing: Snowflake, BigQuery, Redshift, Delta Lake
Visualization & BI: Tableau, Power BI, Looker
DevOps & Deployment: Git, Docker, Kubernetes, CI/CD pipelines
Collaboration: Jira, Confluence, design tools
Data & Analytics: Python, SQL, Spark, Kafka, TensorFlow, PyTorch, scikit-learn, XGBoost Technologies
