Description

You will design and implement large-scale data automation frameworks and data platform architectures.

Responsibilities

• Define and design large-scale data automation frameworks.
• Implement data quality engineering principles including profiling, validation, cleansing, and anomaly detection.
• Lead and mentor technical teams to foster technical excellence.
• Integrate quality gates into CI/CD pipelines using DevOps practices.
• Articulate complex technical concepts to senior leadership and non-technical stakeholders.

Required Skills

• 15+ years of progressive experience in software engineering.
• 5+ years in a Technical Architect, Lead Data Architect, or Principal Data Engineer role.
• Expert-level proficiency in Apache Spark including PySpark, Scala, or Java.
• Deep understanding of Spark SQL, DataFrames, and Datasets.
• Hands-on experience with distributed storage including Hadoop, HDFS, S3, Delta Lake, or Apache Iceberg.
• Experience with streaming technologies such as Apache Kafka or AWS Kinesis.
• Extensive experience with cloud data platforms on AWS, Azure, or GCP.
• Expert-level Python for data engineering and automation.
• Expert-level Advanced SQL for complex data analysis and optimization.
• Bachelor’s or master’s degree in computer science, Engineering, or a related quantitative field.

Preferred Skills

• Experience with GCP tools including Dataproc, BigQuery, and Cloud Storage.

Key Skills

Education

Bachelor’s or master’s degree in computer science