16 days ago
Remote, MexicoStaff+
Responsibilities
- Lead architectural decisions and design reviews for scalable, reliable, and cost-effective data platform components.
- Design and implement production batch pipelines using Snowflake and dbt across product telemetry, marketplace signals, employer activity, and candidate behavior.
- Establish standards for schema design, dimensional modeling, data contracts, data quality, lineage, access control, and cataloging.
- Architect streaming and near-real-time data infrastructure using technologies such as KSQL and Apache Flink.
- Build and support feature stores, training data pipelines, and inference-ready datasets for AI and machine learning systems.
- Own observability, alerting, runbooks, failover strategies, reprocessing protocols, SLA monitoring, and anomaly detection for tier-1 pipelines.
- Partner with engineering, product, analytics, and platform teams to deliver reliable and well-modeled data products.
- Mentor data engineers through code reviews and design guidance while setting technical standards across teams.
- Identify and resolve complex integration, performance, reliability, and architectural issues through hands-on implementation.
- Ensure data systems support privacy, compliance, and applicable regulations including CCPA and GDPR.
Requirements
- 10+ years of data engineering experience, including at least 3–5 years operating at a principal or staff individual-contributor level in a startup environment.
- Required production-grade experience designing, owning, and scaling Snowflake and dbt.
- Experience with AWS at scale, including cloud data architecture, cost governance, and infrastructure-as-code.
- Strongly preferred production experience with real-time streaming technologies such as KSQL or Apache Flink.
- Terraform experience is a plus.
- DataOps experience with CI/CD for data pipelines, automated testing frameworks, and deployment practices for production data infrastructure.
- Significant demonstrated use of AI tools such as Claude, Copilot, Cursor, or similar in daily engineering work.
- Expert-level proficiency in Python and SQL, with strong command of distributed systems, data pipeline design, and performance optimization.
- Strong experience with dimensional modeling, schema design, data contracts, data governance, lineage, cataloging, access control, and quality monitoring.
- Experience building data infrastructure that supports ML/AI systems, including feature pipelines, training data management, and inference-layer data serving.
- Demonstrated experience setting technical standards across teams through RFCs, architecture reviews, or platform migrations.
- Familiarity with real-time and near-real-time architectures, streaming, event-driven design, and CDC.
- Exceptional communication and stakeholder-management skills, including explaining complex infrastructure trade-offs to product and business audiences.
Benefits
- Remote-first work arrangement with flexible working hours across multiple time zones.
- Savings Fund (Fondo de Ahorro).
- Bonus and 30-day El Aguinaldo.
- 20 days of paid time off.
- Medical coverage.
- Parental leave.
Tech Stack
Categories
Data Engineering
