About this role

Develop and implement real-time data ingestion and processing systems.
Design, build, and operationalize large-scale enterprise data solutions and applications.
Create and manage production data pipelines, from ingestion to consumption, within a big data architecture using PySpark.
Leverage expertise in Python, Airflow, SQL, and cloud platforms to build robust solutions (strong knowledge of these is essential).
Translate complex business challenges into scalable and efficient technical solutions.
Work collaboratively with a high-performing data engineering team, owning the full lifecycle of solution implementation.

Bachelor's Degree in Computer Science, Information Technology, or a similar discipline.
8+ years of professional experience in data engineering or related fields.
Proven experience with ETL processes, data integration, and handling large-scale datasets using PySpark.
Strong understanding of data engineering concepts like ETL, near/real-time streaming, data structures, and workflow management.
Experience with version control tools like Git/GitHub.
Proficiency in SQL and Data Warehouse concepts
Experience working with SQL or NoSQL databases like Cassandra, MongoDB, or HBase.
Knowledge of AWS technologies such as EMR, RedShift, Kinesis, Lambda, Glue, S3 IAM, CloudWatch, and big data tools like Hadoop/EMR, Hive, and Sqoop.

Staff Software Engineer, Data Platform

Key skills