Data Engineer

🛠️ Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.

• Build and optimize enterprise data warehouses and data lake solutions.

• Develop high-performance data processing solutions using Spark/PySpark.

• Integrate data from multiple internal and external sources.

• Optimize SQL queries and improve data pipeline performance.

• Implement batch and real-time data processing solutions.

• Work with Data Scientists, BI Developers, and Software Engineers to support analytics initiatives.

• Ensure data quality, governance, security, and compliance.

• Deploy and maintain cloud-based data solutions.

• Perform code reviews and mentor junior team members.

• Troubleshoot production issues and implement performance improvements.

• Follow Agile development methodologies and DevOps best practices.

✅ Required Skills Category Skills Experience 12+ Years in Data Engineering Programming Python, SQL, PySpark, Scala (Preferred) Big Data Apache Spark, Hadoop, Hive Data Integration ETL/ELT, Apache Airflow Streaming Apache Kafka Cloud AWS / Azure / GCP Data Warehouse Snowflake, Redshift, BigQuery, Azure Synapse Data Lake Delta Lake, Apache Iceberg Databases PostgreSQL, MySQL, SQL Server, MongoDB DevOps Git, Docker, Kubernetes, Jenkins Operating System Linux / Unix APIs REST APIs Data Modeling Star Schema, Snowflake Schema, Dimensional Modeling Performance SQL Optimization, Spark Performance Tuning Methodology Agile / Scrum 🎓 Qualifications

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

• Relevant cloud or data engineering certifications are an advantage.

⭐ Preferred Skills

• Experience with Databricks.

• Experience with dbt and Terraform.

• Knowledge of CI/CD pipelines.

• Experience with real-time streaming architectures.

• Exposure to machine learning data pipelines.