Softgic
Cloud • Cybersecurity • Artificial Intelligence
Softgic is a technology company focused on driving digital and cognitive transformation for its clients. With over 12 years of experience, Softgic provides innovative and tailored solutions across various areas, including software development, cloud data security, the metaverse, and automation. Their approach combines design, coding, and implementation to enhance business processes and ensure security and efficiency in technological infrastructure.
51 - 200 employees
Founded 2011
🔒 Cybersecurity
🤖 Artificial Intelligence
Data Engineer, Python, AWS, Data Pipelines
Job not on LinkedIn
October 23
Softgic
Cloud • Cybersecurity • Artificial Intelligence
Softgic is a technology company focused on driving digital and cognitive transformation for its clients. With over 12 years of experience, Softgic provides innovative and tailored solutions across various areas, including software development, cloud data security, the metaverse, and automation. Their approach combines design, coding, and implementation to enhance business processes and ensure security and efficiency in technological infrastructure.
51 - 200 employees
Founded 2011
🔒 Cybersecurity
🤖 Artificial Intelligence
📋 Description
• Design and maintain scalable ETL/ELT pipelines in Python, integrating structured and semi-structured data (JSON, CSV, XML) across Snowflake, MongoDB, Postgres, and AWS services (S3, Glue, Lambda, EC2, EMR, Redshift, RDS). • Build and optimize transformation and orchestration workflows using DBT, Airflow, Prefect, or Dagster. • Implement data governance, quality checks, and security best practices throughout data pipelines. • Extend and optimize the Elastic Hierarchy framework to harmonize financial data from various systems. • Collaborate with analysts, ML engineers, and product teams to deliver business-ready datasets and data solutions.
🎯 Requirements
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. • Strong Python programming skills with experience in data processing and automation. • Proven experience with the AWS data ecosystem, including S3, Glue, Lambda, EMR, EC2, Redshift, and RDS. • Hands-on experience with Snowflake, MongoDB, and Postgres databases. • Proficiency with DBT or similar data transformation tools, and with orchestration frameworks such as Airflow, Prefect, or Dagster. • Knowledge of data mapping, attribution, or reconciliation (experience in financial services is a strong plus). • Understanding of hybrid/on-premise deployment models within enterprise environments. • Excellent English communication skills and ability to collaborate effectively within distributed teams.
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