Data Engineer

Andela exists to connect brilliance and opportunity. Since 2014, we have been dedicated to breaking down global barriers and accelerating the future of work for both technologists and organizations around the world. For technologists, Andela offers competitive long-term career opportunities with leading organizations, access to a global community of professionals, and educational opportunities with leading technology providers. At Andela, we’re deeply passionate about creating long-lasting and transformative growth opportunities for all - and doing it in an E.P.I.C. way! We’re excited to continue building our remote-first team with incredible people like you. After applying for this role, you will join our Andela Community of brilliant technologists by passing a technical screening and live interview. As a community member, you’ll have access to a multitude of exclusive technologist roles. Join Andela today to access this opportunity and more in our global marketplace! Our roles are typically filled at lightning speed, so if you’re considering applying, get your application in quickly!

About the client The client operates a global employment technology platform that connects millions of job seekers with companies through a large-scale search engine for job opportunities. The organization is highly data-driven and focuses on building reliable, high-availability systems to support large volumes of traffic and data processing. Their mission centers on improving access to employment through technology, analytics, and scalable infrastructure. The culture emphasizes large-scale data engineering, experimentation, and operational excellence across distributed systems.

About the role We are looking for a Data Engineer with strong experience in modern data platforms to support a large-scale migration initiative to Snowflake. The role focuses on migrating approximately 1,000 datasets while adapting existing pipelines and workflows to a new architecture. You will work with orchestration tools and cloud-based data lakes to ensure reliability, performance, and data quality. The architecture is already defined internally, so the role is execution-focused and highly hands-on. This position requires strong expertise in SQL, Python, and distributed data processing tools.

Responsibilities Execute the migration of approximately 1,000 datasets to Snowflake. Adapt and refactor existing data pipelines to align with the Snowflake architecture. Develop and test pipelines using Python and Apache Airflow. Support workflow orchestration, scheduling, and monitoring in Apache Airflow. Ensure data quality, reliability, and performance across migrated datasets. Work with data stored in AWS S3 data lakes, including partitioning and lifecycle policies. Implement testing strategies for data pipelines. Collaborate with internal teams following the predefined architecture and technical approach. Work with streaming or event-driven data using Apache Kafka. Support distributed processing workloads using Apache Spark.

Must have skills 7+ years of experience in Data Engineering or related roles. 7+ years of experience with Python (7–9 years). Advanced SQL (complex joins, CTEs, window functions, performance tuning). Snowflake (data modeling and pipeline integration). Apache Airflow (pipeline development and orchestration). Apache Spark (distributed data processing). Apache Kafka. AWS experience, especially S3 (data lakes, partitioning, lifecycle policies). Experience in defining and implementing testing strategies for data pipelines. Hands-on experience building and maintaining production-grade data pipelines.

Nice to have skills Hands-on experience with Snowflake migrations (data warehouse modernization, performance optimization, cost optimization). Experience with Apache Airflow migrations (version upgrades, executor changes, or platform migrations).

Conditions Remote role (100% remote). Primary timezone: UTC-6 with at least 8 hours of overlap required. Contractor role (initially 3-month contract, but renewable: expected overall duration up to 12 months). Full-time dedication (40 hours/week). BYOD required (bring your own device).

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