TO APPLY, ALL APPLICANTS MUST SUBMIT RESUME TO HTTPS://WWW.JOBPOSTINGTODAY.COM/ AND REFERENCE ID#27778. Job Description: Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Design and refactor data marts to enhance data quality, discovery and exploration. Build the semantic layer to ensure it effectively translates complex data from the data marts into business terms. Introduce automation mechanisms to enforce data best practices and optimal design principles. Develop new data pipelines and ETL processes, and enhance the efficiency of existing ones to reduce cost and improve landing times and SLAs Monitor and optimize the performance of queries, ensuring fast response times and efficient data retrieval. This may involve indexing, partitioning, and tuning the underlying data models. Build systems to automate monitoring and alerting for data anomalies. Build internal data products aimed at streamlining workflows, minimizing manual interventions and driving operational efficiencies. Work alongside fellow data engineers and cross-functional partners from Data Science, Data Platform, Machine Learning, and various analytics teams to ensure alignment on data needs and deliverables. Maintain clear and comprehensive documentation of processes to facilitate smooth team collaboration and hand-offs. Telecommuting permitted. Job Requirements: Masters or foreign equivalent degree in Computer Science, Software Engineering, Statistics, Data Science, Data Analytics, or in related field.Special Requirements: Position requires experience, knowledge or coursework in each of the following skills: 1. Designing scalable, modular data models and modern data warehouse/lake architectures using Snowflake or Databricks;2. SQL for data transformation and optimization; 3. Python scripting, automation, and object-oriented programming for building scalable frameworks;4. Developing and maintaining robust ETL/ELT pipelines using dbt and Airflow to ensure reliable and performant data workflows;5. Designing and optimizing prompts for large language models to support internal tooling and improve accuracy and relevance of AI-generated outputs;6. Building insightful dashboards using Looker, Tableau, or Superset, and Python libraries such as Matplotlib or Plotly;7. GitHub-based version control, CI/CD workflows, and containerization tools including Docker;8. Cloud platforms including AWS or GCP.Employment and background checks may be required.