Software Engineering lead Analyst_97757

Key Skills: Pyspark, Databricks, Python, AWS Roles and Responsibilities: Develop and optimize data processing logic using Python and PySpark (hands-on development, 3+ years) Build and maintain scalable data pipelines using Databricks Work with SQL for data transformation, analysis, and pipeline support (2+ years) Use cloud platforms (AWS preferred) for deploying and managing data solutions Contribute to AI/LLM ecosystem-based solutions (OpenAI, LangChain, Vector DBs, etc.) Apply DevOps practices for deployment, monitoring, and operational stability of data systems Participate in Agile teams (Scrum/Kanban) for iterative delivery and collaboration Use Git/SVN for version control and collaborative development Implement automation and scripting using Python and related tools Work with workflow orchestration tools like Airflow where applicable Contribute to continuous improvement and modernization of data engineering practices Skills Required: Relevant experience in Data Engineering / Analytics Engineering roles (formal degree not strictly specified) 5-8 years of experience in data engineering or related domains Strong hands-on experience with Python and PySpark (minimum 3+ years preferred) Experience building and maintaining data pipelines using Databricks Strong SQL skills (at least 2+ years of experience) Understanding of data modeling, transformation, and processing frameworks Exposure to cloud platforms (AWS preferred, minimum 1+ year experience) Familiarity with AI/LLM ecosystem tools such as OpenAI, LangChain, or Vector Databases (preferred) Understanding of DevOps practices including CI/CD, monitoring, and deployment Experience working in Agile environments (Scrum or Kanban) Knowledge of version control tools like Git or SVN Exposure to workflow orchestration tools like Apache Airflow (preferred) Strong problem-solving, analytical, and debugging skills Education: Relevant experience in data engineering and analytics engineering (degree details not specified).