Design, build, and maintain scalable data pipelines to ingest, process, and store data from various sources. Implement data quality checks, and monitor to ensure accuracy and integrity.
Write complex SQL queries for data extraction and transformation to enable ad-hoc analysis and automated reporting. Conduct quantitative analysis to support business decisions.
Develop and manage scalable data foundations and models specifically designed to support AI/ML initiatives and AI-driven insights.
Develop, test, and deploy intelligent agents using Python and the Google a
gent development kit (ADK) framework to automate tasks like data analysis and system orchestration.
Partner with executive stakeholders and data scientists.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
3 years of experience in a data engineering, data infrastructure, or data analytics role.
Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript.
Preferred qualifications:
Experience with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.
Experience with data analysis, including statistics, and ML model development (data preparation, model selection, evaluation, tuning).
Experience in scripting languages like Python for data manipulation, analysis, and automation.
Ability to monitor, troubleshoot, and tune data systems and pipelines to improve efficiency.
Ability to develop tools and systems to automate data processes, and increase overall efficiency, with proficiency in programming languages (e.g., SQL, Python), producing readable and well-structured code.
Ability to deliver and maintain data projects from conception to production.
