Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong Tier-1 brand, popular data engineer title, mid-level experience, metro location, and broad skills increase competition.
Core data engineering skills are transferable across industries, though HR/enterprise domain preference adds some domain sensitivity.
Explicit 5+ years requirement plus mandatory tech stacks, cloud experience, and domain expectations raise shortlisting strictness.
Lead large-scale data platform modernization initiatives from legacy systems to cloud-native AI-enabled platforms focused on HR Data.
Design, develop, and optimize scalable data pipelines and Lakehouse architectures using Python, PySpark, SQL, and ETL/ELT frameworks.
Collaborate with stakeholders to deliver strategic outcomes in migration, data quality, automation, and self-service analytics while ensuring business continuity.
Minimum 5+ years of Database Engineering experience or equivalent via work experience, training, military, or education.
Strong hands-on expertise required in Python, Apache Spark/PySpark, SQL, ETL/ELT design, and API integration.
Experience leading enterprise-scale data modernization initiatives and migrating large-scale data environments from on-premise to cloud.
Relevant degree in Computer Science, Engineering, Data Science, or related field preferred; cloud platform experience (Azure, Microsoft Fabric, Databricks, GCP) is expected.
Deep expertise in Cloud Data Platforms with Lakehouse and Medallion Architecture patterns and data governance frameworks.
Proven leadership in enterprise-scale modernization projects involving legacy data warehouses, Hadoop, and adoption of AI-powered engineering practices.
Experience in HR, Workforce, Talent, Payroll, or Enterprise Data domains with capabilities in AI/ML application integration and cloud-native automation frameworks.