Role Overview

We are seeking an experienced Senior Data Engineer to design, build, and maintain scalable data pipelines and cloud-based data solutions. The role covers data ingestion, processing, storage, orchestration, and delivery to Analytics and BI teams within an AWS-based data ecosystem.

Responsibilities

• Design and maintain batch and real-time data pipelines.
• Integrate data from APIs, databases, files, and external sources using Apache Kafka and AWS services.
• Develop and orchestrate workflows with Apache Airflow, including monitoring, retry mechanisms, and alerting.
• Build and optimize distributed data processing solutions using Apache Spark, PySpark, Spark SQL, and AWS EMR.
• Manage Data Lake and Data Warehouse environments using Amazon S3 and Amazon Redshift.
• Design analytical data models, including Star and Snowflake schemas.
• Prepare and optimize datasets for Analytics and Business Intelligence use cases, particularly for Qlik.
• Develop high-quality solutions using Python and SQL, following best practices for Git, code reviews, testing, and documentation.
• Ensure data quality, security, governance, and compliance, including AWS IAM, encryption, and access control.
• Monitor production data pipelines and proactively address operational and performance issues.

Mandatory Requirements

• Proven experience as a Senior Data Engineer or in a similar role.
• Advanced proficiency in Python and SQL.
• Hands-on experience with:
  • Apache Spark / PySpark
  • Apache Kafka
  • Apache Airflow
• Strong AWS experience, particularly with:
  • Amazon S3
  • Amazon Redshift
  • AWS EMR
• Experience with Data Lakes, Data Warehouses, ETL/ELT processes, and data modeling.
• Strong understanding of batch and streaming architectures.
• Experience with performance optimization, Git, testing practices, and production support.

Technical Stack

• Python
• SQL
• Apache Spark / PySpark
• Apache Kafka
• Apache Airflow
• AWS EMR
• Amazon S3
• Amazon Redshift
• Git
• Qlik