EOS MSP
Data Engineer
Job Description Summary: The Data Engineer Role da will combine data engineering and stewardship to build scalable data pipelines and data foundations for reporting, analytics, automation and AI use cases. The role owns delivery across integration, quality, metadata, lineage, access and operational reliability.
Key Responsibilities
DATA ENGINEERING
Design, build and optimize scalable ETL/ELT pipelines and data models.
Develop curated datasets, semantic layers and reusable data products.
Implement automated testing, reconciliation, observability and exception handling.
Optimize data processing for performance, maintainability, reliability and cost.
Translate business requirements and source-to-target mappings into production solutions.
AI READINESS
Maintain metadata, definitions, catalogue entries, lineage and lifecycle documentation.
Define and monitor data quality rules; lead root-cause analysis and remediation.
Apply data access, classification, privacy, retention and permitted-use controls.
Prepare traceable, permission-aware datasets for analytics, machine learning and generative AI.
Partner with data science teams on data suitability, bias, drift, reproducibility and evaluation needs.
Skills Required:
Technical Skills: SQL, Snowflake, Python, Data Engineering, ETL/ELT Development, Data Modeling, Cloud Data Platforms, Data Integration, API Integration, Data Warehousing, Power BI, Azure Data Services, Data Pipeline Development, Performance Optimization, Automation, CI/CD, Git, DataOps, Data lineage, Cataloguing, Metadata creation.
Generative AI & RAG Technologies – LLM fundamentals, embeddings, vector databases, Retrieval-Augmented Generation (RAG), and unstructured data processing.
Educational Qualifications & Minimum Work Experience
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Analytics or a related field.
5+ years of experience in data engineering, data management, stewardship or analytics enablement.
Advanced SQL and hands-on experience with cloud data platforms and ETL/ELT frameworks.
Proficiency in Python for data processing, automation, validation and integration.
Experience with data modeling, APIs, orchestration, Git, CI/CD, testing and production monitoring.
Additional qualifications are as follows:
Snowflake, Databricks, Azure data services, Power BI, Dataiku or comparable technologies.
Data-product operating models, semantic layers, taxonomies, ontologies, MLOps or regulated environments
Strong data engineering background and problem-solving skills, with the ability to translate complex data into actionable insights and aligning them with business priorities to drive impactful results
Experience in preferably Healthcare Industry