Lead Data Pipeline Engineer
Two Six TechnologiesFull Time
Junior (1 to 2 years)
Candidates should have 4+ years of experience in data architecture, data pipeline, data warehouse modeling, and master data management. Expertise in a full modern data stack including Fivetran, Snowflake, dbt, Airflow, and AWS is required, along with proficiency in SQL, dbt, and Python. Experience with Github, Terraform, and CI/CD is essential, as well as a background in designing, developing, and maintaining large-scale data marts. Excellent written and verbal communication skills are necessary for effective collaboration with both technical and business teams.
The Data Engineer will design, develop, and maintain scalable data pipelines, ensuring reliable data ingestion and transformation. They will collaborate with stakeholders to implement structured data models and optimize database performance. The role involves establishing data validation and monitoring mechanisms to ensure data quality and integrity. Additionally, the Data Engineer will manage cloud-based data storage solutions, enhance processing efficiency, and work closely with cross-functional teams to support their data needs while championing new technologies and methodologies.
Designs and manages co-branded credit card programs
Imprint designs and manages co-branded credit card programs for well-known American brands. By partnering with these brands, Imprint creates credit cards that attract modern consumers, aiming to enhance the value of their partners' customer relationships. The company focuses on increasing metrics such as average spending, shopping frequency, and annual sales for cardholders. Imprint's process is notably faster than traditional credit card issuers, allowing them to launch new programs in about three months instead of the typical 18 months. Additionally, Imprint prioritizes customer service, offering a premium experience to cardholders. The goal of Imprint is to provide brands with effective credit card solutions that drive customer loyalty and sales.