Data Engineer II

Experteer Overview In this Data Engineer II role, you will build and validate data pipelines for enterprise assets within MetLife’s Data Office. You’ll focus on data quality, validation, and scalable processing in Azure Data Factory and Databricks, partnering across engineering, analytics, and operations. The position offers hands-on engineering with growth into broader pipeline improvement and platform care. You’ll contribute to a data-driven culture that powers analytics and informed decision-making, in a hybrid Cary, NC setting. Compensation / Benefits * Perform data validation for enterprise assets including source-to-target validation, profiling, anomaly detection, and defect analysis * Develop automated validation, testing, and data quality controls using Python, PySpark, Spark SQL, SQL, and related frameworks * Build, enhance, and support ETL/ELT pipelines using Azure Data Factory, Databricks, Python, PySpark, and Spark SQL with focus on validation/quality * Troubleshoot data issues, analyze root causes, document findings, and collaborate with Business/Tech/Operations/Data & Analytics teams to resolve defects * Design, develop, and optimize scalable data processing and validation solutions for reporting, analytics, and operational decision-making * Implement and support Delta Lake and Lakehouse architectures for reliable, scalable data processing * Ensure data processing/validation solutions comply with security, quality, and operational standards * Contribute to reusable validation frameworks, engineering standards, playbooks, and platform improvements Tasks * Bachelor’s or Master’s degree in computer science, engineering, information systems, mathematics, statistics, operations research, or related quantitative field, or equivalent experience * 3-5 years of experience in data engineering, analytics engineering, data validation engineering, or related disciplines * Strong hands-on experience with Python, PySpark, Spark SQL, and/or SQL * Experience building, testing, validating, and supporting ETL/ELT pipelines using Azure Data Factory, Databricks, and Delta Lake architectures * Experience developing, troubleshooting, and optimizing scalable cloud-based data solutions in Azure, including data quality, reconciliation, and validation activities Key requirements * Comprehensive health plan (medical, vision, dental) * Disability and life insurance * 401(k) with employer matching * Tuition assistance * Parental leave * Paid time off and holidays

comply analyze root causes, document findings, and collaborate with Business/Tech/Operations/Data & Analytics teams to resolve defects * Design, develop, and optimize scalable data processing and validation solutions for reporting, analytics, and operational decision-making * Implement and support Delta Lake and Lakehouse architectures for reliable, scalable data processing * Ensure data processing/validation solutions comply with security, quality, and operational standards * Contribute to reusable validation frameworks, engineering standards, playbooks, and platform improvements Tasks * Bachelor’s or Master’s degree in computer science, engineering, information systems, mathematics, statistics, operations research, or related quantitative field, or equivalent experience * 3-5 years of experience in data engineering, analytics engineering, data validation engineering, or related disciplines * Strong hands-on experience with Python, PySpark, Spark SQL, and/or SQL * Experience building, aaaa plan validating, and supporting ETL/ELT pipelines using Azure Data Factory, Databricks, and Delta Lake architectures * Experience developing, troubleshooting, and optimizing scalable cloud-based data solutions in Azure, including data quality, reconciliation, and validation activities Key requirements * Comprehensive health plan (medical, vision, dental) * Disability and life insurance * 401(k) with employer matching * Tuition assistance * Parental leave * Paid time off and holidays