Data Engineer (AWS, SQL)

Job Description – Data Engineer Position-Data Engineer

Experience-5 – 10 Years

LOCATION: NEWZEALAND (ON-SITE OPPORTUNITY)

Job Summary

We are seeking an experienced Data Engineer with strong expertise in building scalable and high-performance data platforms. The ideal candidate will have hands-on experience with Apache Airflow, Apache Spark, PySpark, Scala, Databricks, Docker, and Advanced SQL , with a proven track record of developing and optimizing large-scale data pipelines and ETL processes.

Key Responsibilities

Design, develop, and maintain scalable data pipelines and data processing frameworks.

Build and orchestrate ETL/ELT workflows using Apache Airflow .

Develop data engineering solutions using Apache Spark, PySpark, and Scala .

Leverage Databricks for large-scale data processing, optimization, and analytics.

Write, optimize, and troubleshoot complex SQL queries involving

Inner, Left, Right, and Full Joins

Self Joins

CTEs and Subqueries

Window Functions

Query Performance Tuning

Develop, deploy, and manage containerized applications using Docker .

Collaborate with cross-functional teams to gather requirements and deliver robust data solutions.

Implement data quality checks, monitoring, and performance tuning.

Ensure data governance, security, and compliance standards are maintained.

Troubleshoot and resolve production issues related to data pipelines and processing jobs.

Mandatory Skills

Apache Airflow – DAG creation, workflow orchestration, scheduling, and monitoring.

Apache Spark – Distributed data processing and performance optimization.

PySpark – ETL development, transformations, and data engineering.

Scala Programming – Strong hands-on development experience with Spark/Scala applications.

Databricks – Notebooks, workflows, Delta Lake, and cluster management.

Advanced SQL

Strong expertise in Joins (Inner, Outer, Left, Right, Full, Self)

Window Functions

Query Optimization

Data Modeling Concepts

Performance Tuning

Docker

Containerization

Image Creation and Management

Docker Compose

Deployment and Troubleshooting

Python Programming

Git Version Control

Preferred Skills

Delta Lake

Azure, AWS, or GCP

Kafka or Streaming Technologies

CI/CD Pipelines (Azure DevOps, Jenkins, GitHub Actions)

Data Warehousing Concepts

Linux/Unix Administration

Lakehouse Architecture

Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

5–10 years of experience in Data Engineering and Big Data technologies.

Strong expertise in Spark ecosystem and distributed computing.

Experience building enterprise-scale data platforms and ETL solutions.

Excellent analytical, problem-solving, and communication skills.