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

Data Engineer