Minimum Qualifications
- Bachelor's Degree in Engineering, Computer Science, CIS, or related field (or equivalent work experience in a related field)
- 2 years of experience in Data, Platform Engineering, Data Warehousing/ETL, Cloud Platform or Software Engineering
- 1 year of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC)
Preferred Qualifications
- Master's Degree in Computer Science, CIS, or a related field
- 2 years of IT experience in developing and implementing business systems within an organization
- 4 years of experience with technical documentation in a software development environment
- 2 years of experience with Big Data technologies - both On-Premises and Cloud
- 2 years of experience with Hadoop and Spark Experience with application and integration middleware
- Experience with database technologies
Data Engineering
- 2 years of experience with Hadoop or any Cloud Big Data components (specific to the Data Engineering role), preferably Google Cloud Platform
- Expertise in Java/Scala/Python, SQL, Scripting, Hadoop (Sqoop, Hive, HDFS), Spark (Spark Core & Streaming), Kafka or equivalent Cloud Big Data components
- Proficiency in programming languages: Scala, Python, Java, and Big Data technologies, both On-Premises and Cloud
- Knowledge of data modeling and database design
- Hands-on experience with GCP services: BigQuery, Dataflow, Dataproc, Composer/Airflow, Google Storage, Pub-Sub
- Experience with CI/CD processes: Jenkins, Cloud build tools, Docker, Kubernetes, Artifact Registry
- Ability to design scalable data architectures on GCP using services like Dataproc, BigQuery, and Composer
- Experience with ETL (Extract, Transform, Load) processes
- Responsible for optimizing data pipelines and queries for performance, troubleshooting issues, and proactive monitoring
- Knowledge of quality checks, data governance, and security policies