Job
Description
What You'll Be Doing: Building the data engineering and analytics infrastructure for our new Enterprise Data Platform using Snowflake and Fivetran. Leading the development of data models, algorithms, data pipelines, and insights to enable data-driven decision-making. Collaborating with team members to shape the design and direction of the data platform. Working end-to-end on data products, from problem understanding to developing data pipelines, dimensional data models, and visualizations. Providing support and advice to business users, including data preparation for predictive and prescriptive modeling. Ensuring consistency of processes and championing best practices in data management. Evaluating and recommending new data tools or processes. Designing, developing, and deploying scalable AI/Generative AI and machine learning models as needed. Providing day-to-day production support to internal business unit customers, implementing enhancements and resolving defects. Maintaining awareness of emerging trends in AI, Generative AI, and machine learning to enhance existing systems and develop innovative solutions. The Impact You Will Have: Driving the development of a cutting-edge data platform that supports enterprise-wide data initiatives. Enabling data-driven decision-making across the organization through robust data models and insights. Enhancing the efficiency and effectiveness of data management processes. Supporting business users in leveraging data for predictive and prescriptive analytics. Innovating and integrating advanced AI and machine learning solutions to solve complex business challenges. Contributing to the overall success of Synopsys by ensuring high-quality data infrastructure and analytics capabilities. What You'll Need: BS with 5+ years of relevant experience or MS with 3+ years of relevant experience in Computer Sciences, Mathematics, Engineering, or MIS. 5 years of experience in DW/BI development, reporting, and analytics roles, working with business and key stakeholders. Advanced knowledge of Data Warehousing, SQL, ETL/ELT, dimensional modeling, and databases (e.g., mySQL, Postgres, HANA). Hands-on experience with modern data stack tools, including cloud data warehouses (Snowflake), transformation tools (dbt), and cloud providers (Azure, AWS). Experience with data ingestion tools (e.g., Fivetran, HVR, Airbyte), CI/CD (GitLab, Kubernetes, Airflow), and data catalog tools (e.g., Datahub, Atlan) is a plus. Proficiency in scripting languages like Python, Unix, SQL, Scala, and Java for data extraction and exploration. Experience with visualization tools like Tableau and PowerBI is a plus. Knowledge of machine learning frameworks and libraries (e.g., Pandas, NumPy, TensorFlow, PyTorch) and LLM models is a plus. Understanding of data governance, data integrity, and data quality best practices. Experience with agile development methodologies and change control processes.
