Data Engineer

Job Description: Design, build, and maintain scalable data pipelines supporting batch, near-real-time, API, and streaming ingestion patterns from enterprise and external data sources Develop and optimize ETL/ELT workflows across raw, enriched, and curated data layers within a cloud-based lakehouse environment Implement and manage data storage solutions for structured, unstructured, and semi-structured data Collaborate with data architects to translate platform designs into reliable, production-grade implementations Apply and enforce data governance standards including data quality checks, lineage tracking, classification tagging, and access controls Build and maintain reusable data products that serve AI/ML and analytics consumers across multiple business domains Monitor pipeline health and implement observability, logging, and alerting practices to ensure data reliability Work with orchestration and scheduling tools to automate and manage complex data workflows Partner with security teams to ensure pipelines adhere to data classification and compliance requirements Contribute to platform documentation, code reviews, and engineering best practices Job Requirements: 4+ years of experience in data engineering, ETL development, or a closely related role Strong hands-on experience building data pipelines on cloud platforms Proficiency with ETL/ELT frameworks and workflow orchestration tools Experience working with lakehouse or data lake architectures and multi-zone storage patterns (landing, raw, enriched/curated) Solid programming skills in Python and/or SQL ; familiarity with CLI and infrastructure-as-code tools is a plus Experience with structured, unstructured, and semi-structured data processing Familiarity with data governance, data quality, and metadata management concepts Understanding of streaming and real-time data processing patterns Ability to work effectively in a large, cross-functional, and matrixed enterprise environment Strong problem-solving skills and attention to detail