Resumen del rol
Publicado vía Mainder
We are seeking a Senior Data Engineer to join a 3-month remote project in a multicultural team. You will operate across both sides of data engineering: keeping data platforms reliable and performant, while also designing modern, event-driven data systems that power AI-enabled products and analytics use cases.
In this role, you will work across multiple projects, ranging from stabilizing and optimizing existing data stacks to designing new pipelines for real-time behavioral analytics, machine learning, and AI workloads. The ideal candidate is comfortable switching between operational excellence and greenfield architecture, depending on project needs.
Data Platform Operations
Monitor and maintain data ingestion and transformation pipelines to ensure reliability and data quality.
Triage alerts, debug pipeline failures, and resolve production data issues efficiently.
Tune warehouse and pipeline performance to improve reliability, scalability, and cost efficiency.
Ensure scheduled jobs and dependencies run consistently and recover gracefully from failures.
Data Architecture & Pipeline Development
Design and build new data infrastructure from scratch when required.
Implement event-driven architectures, real-time data pipelines, and scalable data models.
Develop data pipelines that support analytics, machine learning models, and AI agents.
Balance short-term stability needs with long-term architectural improvements.
Collaboration & Communication
Work independently once context is understood, owning problems end to end.
Communicate clearly with both technical teammates and non-technical stakeholders.
Ask the right questions before implementation to ensure alignment and impact.
Contribute to a culture of reliability, ownership, and continuous improvement.
4+ years of experience in data engineering roles.
Advanced proficiency in SQL and Python.
Strong experience with cloud data warehouses such as Redshift, Snowflake, BigQuery, or similar.
Hands-on experience with orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Experience building and maintaining ETL/ELT pipelines.
Familiarity with data ingestion tools such as Fivetran, Segment, Stitch, or similar.
Experience working in AWS environments, including S3, Lambda, and general cloud infrastructure.
Experience with pipeline monitoring, alerting, and incident response.
Proven ability to debug and resolve production data issues under pressure.
Experience with event-driven architectures (Kafka, EventBridge, or similar).
Familiarity with real-time data processing and streaming systems.
Experience with PostgreSQL and platforms such as Supabase.
Exposure to NoSQL databases (e.g., DynamoDB).
Understanding of data modeling for analytics and machine learning use cases.
Experience supporting ML/AI workloads, feature stores, or feature pipelines.
Exposure to BI tools such as Looker, Metabase, or similar.
Autonomy and strong ownership mindset.
Calm, structured problem-solving in production environments.
Clear communication with technical and non-technical stakeholders.
Ability to balance system stability with innovation.
Curiosity, adaptability, and continuous learning.
We are a technology-driven organization building data-intensive platforms and AI-enabled products through distributed teams. We focus on solving complex problems with reliable, scalable data systems, fostering a culture of ownership, technical excellence, and real-world impact.