Trust Engine: Lead Data Engineer - Python - Python, AWS, Apache Spark, SQL, NoSQL - Finance market

7+ years
Finance
Full Remote
Python
AWS
Apache Spark
SQL
NoSQL

Requirements

Must-haves

- 7+ years of experience in data engineering - Leadership experience - AWS experience - Apache Spark experience - Experience building scalable data pipelines and ETL processes from scratch - Strong proficiency with Python - Proficiency with SQL and relational database technologies - Expertise in distributed systems and data processing frameworks - Expertise in data lake and cloud computing platforms - Deep knowledge of data modeling and data warehousing concepts - Familiarity with data governance, access controls, security, and compliance principles - Ability to optimize data pipelines for performance, scalability, and reliability - Strong problem-solving and analytical skills for complex data engineering challenges - Excellent communication skills in both spoken and written English - Bachelor's Degree in Computer Engineering, Computer Science, or equivalent

Nice-to-haves

- Experience with real-time data processing and streaming frameworks - Knowledge of modern data lake house technologies - Experience with Docker and Kubernetes - Experience with Terraform, Airflow, Pandas, PySpark - Experience with NoSQL databases - Experience with data visualization tools and data exploration techniques - Actively participating in the data engineering community (e.g. making contributions to open-source data engineering projects)

What you will work on

- Develop effective data pipelines and ETL processes for data lake integration - Optimize data infrastructure considering data volume, velocity, and variety - Ensure data architecture performance, reliability, and scalability - Implement data governance practices to ensure data quality, integrity, and security - Adopt optimal engineering practices, methodologies, procedures, and technologies - Work with cross-functional teams to develop data solutions that meet business needs - Stay updated with data engineering trends - Share insights with the team and organization - Effectively communicate technical concepts, solutions, and recommendations to both technical and non-technical stakeholders