Mid Data Engineer (3-month project)

Enroute

1d•Hybrid

About The Position

We love technology, and we enjoy what we do. We are always looking for innovation. We have social awareness and try to improve it daily. We make things happen. You can trust us. Our Enrouters are always up for a challenge. We ask questions, and we love to learn. We pride ourselves on having great benefits and compensations, a fantastic work environment, flexible schedules, and policies that positively impact the balance of work and life outside of it. We care about who you are in the office and as an individual. We get involved, we like to know our people, we want every Enrouter to become part of a great community of highly driven, responsible, respectful, and above all, happy people. We want you to enjoy working with us. We are looking for a Mid/Senior Data Engineer to join our team and support the design, development, and optimization of our data platform. This role will focus heavily on building and maintaining reverse ETL workflows to operationalize warehouse data across downstream systems. You will work closely with Analytics, Product, and Engineering teams to ensure that high-quality, well-modeled data is not only available for reporting, but also activated in business and operational tools.

Requirements

This is a 3-month contract project, with the possibility of extension based on performance and business needs.
3–5+ years (Mid-level) or 5–8+ years (Senior-level) experience in Data Engineering
Strong hands-on experience with SQL and data modeling
Proven past experience implementing reverse ETL solutions (hands-on ownership, not just exposure)
Experience working with modern data warehouses (Snowflake preferred)
Experience building ETL/ELT pipelines using Python and/or SQL-based tools
Experience with orchestration tools (Airflow, dbt, or similar)
Experience working in cloud environments (AWS, GCP, or Azure)
Strong understanding of data quality and monitoring practices

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines.
Implement and manage reverse ETL workflows that sync data from the data warehouse (e.g., Snowflake) into operational systems (CRMs, marketing tools, internal applications, etc.).
Optimize data models to support both analytics and activation use cases.
Ensure data quality, validation, and monitoring across pipelines.
Collaborate with cross-functional teams to translate business requirements into reliable data solutions.
Support performance tuning and cost optimization of warehouse workloads.
Maintain documentation and best practices across data workflows.