This role involves developing custom software solutions by designing, coding, and enhancing system components. You will leverage modern frameworks and agile methodologies to deliver scalable, high-performing solutions precisely tailored to business requirements.

The position offers an opportunity to troubleshoot and optimize existing software, continuously seeking innovative approaches to advance the overall development process and improve functionality and performance.

Design and maintain scalable data pipelines and ETL processes for data collection, processing, and storage. Collaborate with data scientists and business teams to understand data needs and provide reliable data solutions. Optimize data architecture for performance, reliability, and scalability. Implement data quality checks and monitor data integrity. Work with cloud platforms like AWS, GCP, Azure, and big data technologies such as Spark, Hadoop, and Kafka. Manage and optimize databases and data warehouses, including Redshift, BigQuery, and Snowflake. Develop automation tools for data ingestion, transformation, and integration. Document data flows, architecture, and processes. Ensure adherence to data governance and security policies.

A Bachelor's degree in Computer Science Engineering, Information Technology, or a related field is essential. Minimum of 2 years of professional experience in Data Engineering is required, with a preference for candidates with 3 years of experience. Proficiency in programming languages like Python, Pyspark, Java, or Scala is crucial. Experience with SQL and NoSQL databases, as well as ETL tools and data pipeline frameworks like Apache Airflow or Luigi, is necessary. Hands-on experience with cloud data services such as AWS Glue, Google Dataflow, or Azure Data Factory, and familiarity with big data technologies (Hadoop, Spark, Kafka) are expected. Strong problem-solving abilities, meticulous attention to detail, and excellent communication and collaboration skills are vital. Experience with containerization and orchestration tools like Docker and Kubernetes, along with knowledge of machine learning pipelines and data science workflows, is beneficial. Familiarity with data visualization tools such as Tableau or Power BI is a plus.