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Data Engineer (hybrid)

Axelon Services Corporation

  • Aliso Viejo, CA
  • 1 day ago
  • $50 Per Hour

Highlights

Required Skills Data Engineering Data Pipeline Development ETL/ELT Design and Development Data Warehousing Concepts Data Modeling (Dimensional & Relational) Data Lake Architecture Data Quality and Data Governance Cloud Technologies AWS (Glue, EMR, Lambda, S3, Redshift, Athena) or Azure (ADF, Data Lake, Synapse, Databricks) Databases SQL Server Oracle PostgreSQL Snowflake Programming Python SQL PySpark Shell Scripting Big Data Technologies Apache Spark Hadoop Ecosystem Kafka (Preferred) DevOps & CI/CD Git Jenkins/Azure DevOps CI/CD Pipeline Implementation Preferred Qualifications Experience in Healthcare, Health Insurance, or Life Sciences domain. The ideal candidate will have strong expertise in cloud-based data engineering, ETL/ELT development, big data technologies, and data quality practices, with experience working in healthcare environments.

Numbers & Facts

LocationAliso Viejo, CA
Salary$50 Per Hour

Description

Title: Data Engineer (hybrid)
Location: Atlanta, GA

Job Description:
  • We are seeking a skilled Data Engineer to design, develop, and optimize scalable data pipelines and data platforms that support enterprise analytics, reporting, AI/ML initiatives, and healthcare business operations.
  • The ideal candidate will have strong expertise in cloud-based data engineering, ETL/ELT development, big data technologies, and data quality practices, with experience working in healthcare environments. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data. Build and optimize data ingestion frameworks from multiple source systems, APIs, databases, and cloud platforms.
  • Develop and maintain enterprise data models, data warehouses, and data lakes. Ensure high levels of data quality, integrity, security, and governance across data platforms.
  • Collaborate with business analysts, architects, data scientists, and application teams to understand data requirements. Implement data transformation, validation, reconciliation, and monitoring processes. Optimize performance of large-scale data pipelines and database workloads. Support analytics, reporting, AI/ML, and advanced data science initiatives through reliable data delivery. Implement metadata management, lineage tracking, and data cataloging solutions. Troubleshoot production issues and perform root cause analysis for data-related incidents.
  • Participate in code reviews, architecture discussions, and technical design sessions. Ensure adherence to healthcare regulatory and compliance requirements, including HIPAA and data privacy standards.
  • Work with distributed teams across multiple locations and support enterprise-scale data initiatives.
  • Required Skills Data Engineering Data Pipeline Development ETL/ELT Design and Development Data Warehousing Concepts Data Modeling (Dimensional & Relational) Data Lake Architecture Data Quality and Data Governance Cloud Technologies AWS (Glue, EMR, Lambda, S3, Redshift, Athena) or Azure (ADF, Data Lake, Synapse, Databricks) Databases SQL Server Oracle PostgreSQL Snowflake Programming Python SQL PySpark Shell Scripting Big Data Technologies Apache Spark Hadoop Ecosystem Kafka (Preferred) DevOps & CI/CD Git Jenkins/Azure DevOps CI/CD Pipeline Implementation Preferred Qualifications Experience in Healthcare, Health Insurance, or Life Sciences domain.
  • Exposure to AI/ML data pipelines and MLOps frameworks. Knowledge of healthcare data standards such as HL7, FHIR, ICD, and CPT is preferred.
  • Experience with Snowflake, Databricks, or modern cloud-native data platforms. Familiarity with Agile/Scrum methodology.
  • Education Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field.
  • Key Competencies Strong analytical and problem-solving skills. Excellent communication and stakeholder management abilities.
  • Ability to work independently in fast-paced environments. Strong collaboration and mentoring capabilities. Commitment to quality, scalability, and continuous improvement.
Responsibilities
  • Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
  • Build and optimize data ingestion frameworks from multiple source systems, APIs, databases, and cloud platforms.
  • Develop and maintain enterprise data models, data warehouses, and data lakes

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