Data Engineer

RESPONSIBILITIES :

- Design, develop, and maintain scalable, efficient data pipelines to support ETL/ELT processes across multiple sources and systems

- Partner with Data Science, Analytics, and Business teams to understand data needs, prioritize use cases, and deliver reliable datasets and models

- Monitor, optimize, and troubleshoot data jobs, ensuring high availability and performance of data infrastructure

- Build and manage data models and schemas in Redshift and other data technologies, enabling self-service analytics

- Implement data quality checks, validation rules, and alerting mechanisms to ensure trust in data

- Leverage AWS services like Glue, Lambda, S3, Athena, and EMR to build modular, reusable data solutions

- Drive improvements in data lineage, cataloging, and documentation to ensure transparency and reusability of data assets

- Create and maintain technical documentation and version-controlled workflows (e.g., Git, dbt)

- Contribute to and promote a culture of continuous improvement, mentoring peers and advocating for scalable and modern data practices

- Participate in sprint planning, code reviews, and team retrospectives as part of an Agile development process

- Stay current on industry trends and emerging technologies to identify opportunities for innovation and automation

QUALIFICATIONS :

- 6+ years of experience in software/data engineering, with a proven track record of building robust data systems (prior leadership or mentorship experience is a plus)

- Advanced Python, including experience building APIs, scripting ETL processes, and automating workflows

- Expert in SQL, with ability to write complex queries, optimize performance, and work across large datasets

- Hands-on experience with AWS data ecosystem including Redshift, S3, Glue, Athena, EMR, EC2, DynamoDB, Lambda, and Redis

- Strong understanding of data warehousing and data modeling principles (e.g., star/snowflake schema, dimensional modeling)

- Familiarity with dbt Labs and modern ELT/analytics engineering practices

- Experience working with structured, semi-structured, and unstructured data

- Knowledge of data governance, quality assurance, and observability tools to ensure data integrity and trust

- Proficiency in creating technical documentation, SOPs, and maintaining clear communication with cross-functional teams

- A problem-solving mindset, with the ability to take ownership and drive solutions from design to deployment