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About the role
Required Technical Skills Core Platforms Databricks Unity Catalog, Delta Live Tables, Databricks SQL, Spark optimization, job orchestration Tableau calculated fields, LOD expressions, data blending, published data sources, Tableau Server/Cloud Python pandas, NumPy, PySpark, scikit-learn, SQLAlchemy, Airflow/Prefect integration Large Language Models (LLMs) Hands-on familiarity with leading LLMs and their APIs, including but not limited to: SemiKong (semiconductor-domain LLM) preferred differentiator for this role OpenAI GPT-4 / GPT-4o Anthropic Claude (claude-3 / claude-sonnet family) Google Gemini (1.5 Pro / Flash) Meta LLaMA 3 / LLaMA 3.1 Mistral / Mixtral Cohere Command R+ Falcon, Phi-3, and other open-weight models Prompt engineering, RAG architecture, LangChain / LlamaIndex for applied analytics use cases Data SQL Advanced SQL window functions, CTEs, query optimization across Databricks SQL, Snowflake, or similar Data modeling concepts star/snowflake schemas, dimensional modeling Experience with large-scale structured and semi-structured datasets (JSON, Parquet, Delta format) Additional Technical Exposure (Preferred) Cloud platforms AWS (S3, Glue, Redshift), GCP (BigQuery), or Azure (Synapse, ADLS) Version control Git/GitHub for script management and collaboration Basic ML model evaluation and feature engineering in Python Familiarity with CI/CD pipelines for analytics/ML assets Experience Qualifications 10+ years of professional experience as a Data Analyst, Analytics Engineer, or closely related role. Proven track record delivering analytical projects in semiconductor, manufacturing, or hi-tech industry preferred. Strong portfolio demonstrating Databricks and Tableau work at scale. Demonstrated hands-on usage of LLMs in a professional or research context not just theoretical knowledge. Experience working in agile / scrum teams within large enterprise environments. Bachelors degree (or higher) in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field. Professional Competencies Strong business acumen ability to translate data findings into clear, actionable recommendations for non-technical stakeholders. Excellent verbal and written communication skills in English. Self-starter mindset; comfortable with ambiguity and evolving requirements. High attention to detail and commitment to data accuracy and integrity. Collaborative team player with the ability to work across global time zones as needed. Work Conditions Attribute Detail Work Location Applied Materials office, Bangalore on-site every day Remote/Hybrid Option None. Full on-site presence is mandatory. Engagement Model Contract (duration TBD based on project scope) Notice Period Immediate joiners preferred / negotiable Key Responsibilities Design, build, and maintain end-to-end data pipelines and analytical workflows on Databricks (Delta Lake, Spark, MLflow). Develop interactive dashboards and visual reporting layers in Tableau; translate findings into executive-ready narratives. Write production-quality Python scripts for data ingestion, transformation, automation, and model integration. Collaborate with data engineers, product managers, and business stakeholders to define KPIs and data requirements. Apply LLM-powered capabilities (e.g., SemiKong, GPT-4, Claude, Gemini, LLaMA, Mistral) to augment analytics workflows and automate insight generation. Conduct exploratory data analysis (EDA), statistical modelling, and root-cause analysis on large-scale datasets. Ensure data quality, governance, and lineage across analytical assets. Mentor junior analysts and contribute to the teams data literacy initiatives. Participate in cross-functional sprint planning and agile ceremonies as a technical SME. Disclaimer :This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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