Senior Data Integration Specialist ML Engineer

As a Senior Data Integration Specialist at BEAT, you will play a crucial role in designing and implementing rule-based data ingestion systems using cutting-edge data integration strategies. Your responsibilities will include designing validation engines, integrating with MDS API, Snowflake, CRM, and DataBricks, error monitoring, statistical analysis on datasets, and creating robust data pipelines for efficient data consumption. Key Responsibilities: - Build efficient data pipelines and data integration setups for large-scale data platforms on AWS, Azure, and Snowflake. - Design, construct, install, test, and maintain data integration setups like MDS, CRM, or proprietary systems. - Develop validation engines for monitoring sourced data. - Process, cleanse, and verify data integrity for analysis. - Enhance data collection procedures to include relevant information for building analytic systems. - Collaborate with data architects, IT teams, data scientists, etc., to achieve project goals. - Research new uses for existing data and recommend ways to improve data reliability and quality. - Install/update disaster recovery procedures and manage data security requirements. - Ensure all systems meet business/company requirements and industry practices. Qualifications Required: - Graduation in Engineering, Technology, applied mathematics, physics statistics with good business skills. - 3.5+ years of experience in software engineering with knowledge of Vibe coding using Cursor AI. - Expertise in Advanced Python, FastAPI, MongoDB, Elasticsearch/OpenSearch, and Temporal. - Strong AWS/Azure experience. - Proficiency in DevOps/DataOps (CI/CD, Github Actions/Docker/podman). - Experience in administering enterprise data platforms. - Ability to approach data organization challenges while focusing on the important aspects. - Experience with Cloud-based infrastructure and exposure to Data Ops or ML Ops. - Good scripting and programming skills in Python and LLM. - Familiarity with data visualization tools, NoSQL & Vector databases. - Strong problem-solving, communication, negotiation, and analytical skills. Note: No additional details about the company were provided in the job description. As a Senior Data Integration Specialist at BEAT, you will play a crucial role in designing and implementing rule-based data ingestion systems using cutting-edge data integration strategies. Your responsibilities will include designing validation engines, integrating with MDS API, Snowflake, CRM, and DataBricks, error monitoring, statistical analysis on datasets, and creating robust data pipelines for efficient data consumption. Key Responsibilities: - Build efficient data pipelines and data integration setups for large-scale data platforms on AWS, Azure, and Snowflake. - Design, construct, install, test, and maintain data integration setups like MDS, CRM, or proprietary systems. - Develop validation engines for monitoring sourced data. - Process, cleanse, and verify data integrity for analysis. - Enhance data collection procedures to include relevant information for building analytic systems. - Collaborate with data architects, IT teams, data scientists, etc., to achieve project goals. - Research new uses for existing data and recommend ways to improve data reliability and quality. - Install/update disaster recovery procedures and manage data security requirements. - Ensure all systems meet business/company requirements and industry practices. Qualifications Required: - Graduation in Engineering, Technology, applied mathematics, physics statistics with good business skills. - 3.5+ years of experience in software engineering with knowledge of Vibe coding using Cursor AI. - Expertise in Advanced Python, FastAPI, MongoDB, Elasticsearch/OpenSearch, and Temporal. - Strong AWS/Azure experience. - Proficiency in DevOps/DataOps (CI/CD, Github Actions/Docker/podman). - Experience in administering enterprise data platforms. - Ability to approach data organization challenges while focusing on the important aspects. - Experience with Cloud-based infrastructure and exposure to Data Ops or ML Ops. - Good scripting and programming skills in Python and LLM. - Familiarity with data visualization tools, NoSQL & Vector databases. - Strong problem-solving, communication, negotiation, and analytical skills. Note: No additional details about the company were provided in the job description.