Senior Data Engineer

About The Role

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL workflows using orchestration tools such as Apache Airflow or dbt.
• Architect and implement data integration solutions that consolidate data from multiple heterogeneous sources into cloud-based data platforms.
• Work with large-scale distributed data processing systems such as Apache Spark and Kafka to build real-time and batch data pipelines.
• Build and maintain data warehouses, ensuring efficient data modeling, schema design, and performance tuning.
• Collaborate with data scientists and analysts to understand data requirements and deliver reliable datasets to power analytics and ML models.
• Implement best practices for data governance, data quality, and security in compliance with organizational standards.
• Optimize data workflows for efficiency and scalability in cloud environments such as AWS, GCP, or Azure.
• Monitor and troubleshoot production data pipelines, proactively identifying and resolving bottlenecks or failures.
• Document data engineering processes, data flows, and architecture to facilitate team knowledge sharing.
• Mentor junior data engineers and contribute to continuous improvement of data engineering practices and technologies.
  

Required Skills & Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, or a related discipline.
• Minimum 5 years of professional experience as a Data Engineer or in a similar role.
• Strong proficiency in SQL and Python; familiarity with Scala, Java, or other languages is a plus.
• Hands-on experience with data orchestration tools such as Apache Airflow, dbt, or similar platforms.
• Expertise in cloud data platforms like AWS (Redshift, S3, Glue), GCP (BigQuery, Dataflow), or Azure (Data Factory, Synapse).
• Working knowledge of big data ecosystems, including Apache Spark, Kafka, Hive, and related tools.
• Deep understanding of data warehousing concepts, dimensional modeling, star and snowflake schemas, and data partitioning strategies.
• Experience with data modeling tools like Erwin Data Modeler or MySQL Workbench.
• Strong analytical and problem-solving skills with a proactive, team-focused mindset.
• Familiarity with version control systems (Git) and CI/CD practices for data engineering.