PySpark)

Enroute

About The Position

We love technology, and we enjoy what we do. We are always looking for innovation. We have social awareness and strive 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. At Enroute, we are looking for a Senior Data Engineer to join a growing Data team responsible for designing, building, and evolving scalable data platforms and cloud-native pipelines that support business intelligence, analytics, and operational workloads. The ideal candidate is highly hands-on with Python, Spark/PySpark, Snowflake, and cloud-based data architectures , with strong experience building reliable, production-grade ETL/ELT pipelines and modern data warehousing solutions. This role is ideal for someone who enjoys solving complex data challenges, optimizing performance at scale, and collaborating closely with data scientists, analysts, and engineering teams.

Requirements

5+ years of professional experience in Data Engineering or related fields
Strong experience designing and maintaining scalable data pipelines
Deep understanding of ETL / ELT best practices
Strong experience with large-scale data processing architectures
Proven experience with batch data processing
Strong experience with data warehousing concepts
Advanced Python
Strong hands-on experience with Apache Spark / PySpark
Advanced SQL (complex queries, optimization, transformations)
Strong experience processing large structured and unstructured datasets
Hands-on experience with AWS or Azure
Experience building cloud-native data solutions
Experience with Docker
Experience with CI/CD pipelines
Strong knowledge of Git / version control
Strong hands-on experience with Apache Airflow
Experience designing workflow orchestration pipelines
Scheduling, monitoring, and failure recovery strategies
Strong expertise in Snowflake (MUST HAVE)
Snowflake data warehouse design
Snowflake development
Query and warehouse optimization
Performance tuning and cost efficiency
Cloud data warehouse architecture best practices

Responsibilities

Design, build, and maintain scalable, reliable, and high-performance data pipelines
Develop end-to-end ETL / ELT workflows
Process large-scale datasets using Spark / PySpark
Build and orchestrate cloud-native pipelines in AWS and/or Azure
Design and optimize Snowflake data warehouse solutions
Ensure performance, scalability, governance, and cost optimization
Write and optimize advanced SQL queries
Collaborate with Data Scientists, Analysts, and Software Engineers
Translate business requirements into production-ready data solutions
Ensure data consistency, availability, and quality
Implement CI/CD, Git workflows, and Dockerized deployments
Improve reliability and observability of data platforms