Description

Design and own end-to-end data engineering solutions, including scalable ELT/ETL pipelines, data models, and self-service platforms.

This role is on-site.

Responsibilities

• Architect and optimize high-performance data pipelines using Spark and Airflow for batch and streaming workloads.
• Design robust data models and schemas for Redshift, DynamoDB, MongoDB, and Postgres to ensure fast retrieval and storage efficiency.
• Implement rigorous data quality frameworks to guarantee accuracy, freshness, and trust across all datasets.
• Partner with product engineers and data scientists to define business requirements and build next-generation data systems.
• Maintain full technical ownership of the data lifecycle, including CI/CD deployments via Bitbucket and Git, and participate in on-call rotations.

Required Skills

• Bachelor’s degree in Computer Science or equivalent with 8+ years of professional experience as a Data Engineer.
• Proficient in Python and Scala with rock-solid SQL skills.
• Experience building scalable data pipelines in Spark using Airflow or similar scheduling frameworks.
• Hands-on experience with Databricks and its APIs.
• Deep knowledge of modern databases (Redshift, DynamoDB, MongoDB, Postgres) and data lakes.
• Experience with CI/CD tools like Bitbucket and Git for automated builds and deployments.
• Proven track record working with large-scale, high-performance data processing systems.
• Experience in SaaS companies is preferred.

Preferred Skills

• Experience with Machine Learning workflows and integrating ML models into data platforms.
• Commitment to open-source projects.

Key Skills

Education

Bachelor's degree