Data Engineer II

Roles & Responsibilities:

●Develop the architecture, design, and implementation of Breadfast's data infrastructure, including data warehouses, data pipelines, and data lakes.
●Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and translate them into robust, scalable data solutions.
●Design and develop complex, high-performance ETL/ELT workflows using tools like Airflow, Spark, Kafka, etc.
●Implement data quality frameworks and monitor data integrity across the organization.
●Automate data ingestion, transformation, and reporting processes to improve efficiency and reliability.
●Mentor and guide more junior data engineering team members.
●Stay up-to-date with the latest data engineering trends, technologies, and best practices.
●Contribute to the evolution of Breadfast's data strategy and roadmap.
●Support the productionization and operationalization of machine learning models.

REQUIRED EDUCATION, KNOWLEDGE, AND SKILLS

●3-5 years of hands-on experience as a data engineer or in a similar role.
●Proven expertise in designing and implementing data warehouse architectures using dimensional modeling.
●Strong proficiency in SQL, with the ability to write complex, optimized, and maintainable queries.
●Deep understanding of database internals, including indexing, partitioning, and query optimization.
●Experienced in building scalable and fault-tolerant data pipelines using tools like Apache Airflow, Apache Kafka, Apache Spark, or similar.
●Familiarity with cloud-based data platforms (e.g., GCP, AWS, Azure) and their associated data services.
●Proficient in one or more programming languages (e.g., Python, Scala, Java) for data processing and transformation.
●Expertise in managing and optimizing data workflows, including monitoring, alerting, and incident response.
●Experience in leading and mentoring junior data engineering team members.
●Strong problem-solving skills and the ability to work collaboratively across teams.
●Excellent communication skills, both written and verbal, to effectively translate technical concepts to business stakeholders.
Preferred Skills:

●Experience with NoSQL databases (e.g., MongoDB, Cassandra) for analytics
●Knowledge of machine learning and MLOps concepts and tools
●Proficiency in data visualization tools like Tableau, Power BI, or Looker
●Experience in building and maintaining data catalogs and metadata management systems
●Familiarity with infrastructure-as-code and containerization (e.g., Docker, Kubernetes)