Data Engineer
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Job Description
🛠️ 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.