We are looking for an experienced Data Engineer specialized in Data Integration & Pipelines to build reliable, high-performance data architectures that unify data across diverse enterprise systems.
The core focus of this position is production-grade data engineering, pipeline resilience, and robust systems integration. You will solve complex ingestion challenges, handle strict non-functional constraints, and ensure clean, idempotent, and fault-tolerant data flows into our cloud data warehouse and lakehouse layers.
Key Responsibilities: Design, construct, and maintain resilient data pipelines that ingest and harmonize data from diverse enterprise sources, including ERPs, CRMs, and HR platforms.
Ingest, transform, and model high-volume data within Snowflake, Databricks, and AWS S3.
Architect systems to satisfy stringent non-functional requirements: enforce source-system rate limits, mitigate throttling/blocking, implement exponential backoff/retries, and enforce data consistency checks.
Build robust, resumable ingestion processes so unexpected network or system drops resume without restarting the entire workload.
Ensure all data operations, pipelines, and retry mechanisms are strictly idempotent.
Orchestrate and monitor automated end-to-end workflows, with an emphasis on AWS services such as Step Functions.
Write performant, maintainable, and clean Python and SQL code for ETL/ELT workflows.




Leverage AI coding assistants to accelerate delivery while rigorously auditing, benchmarking, and debugging all generated code before deployment.
Create clear architectural documentation and champion engineering best practices across the team. Required Qualifications: 5+ years of dedicated professional experience in data engineering.
Proven track record integrating third-party enterprise platforms (e.g., Salesforce, Workday, SAP, Net Suite, or equivalent ERP/CRM/HR systems).
Hands-on production experience with Snowflake or Databricks, alongside AWS S3.
Advanced proficiency in Python and SQL.
Demonstrated expertise handling strict non-functional requirements: latency targets, API rate-limiting/throttling management, data integrity audits, and resumable data transfers.
Practical experience designing idempotent data pipelines and failure-recovery mechanisms.
Disciplined engineering judgment to review, validate, and secure AI-generated code (ensuring business logic validity, security standards, and comprehensive test coverage).
Professional working proficiency / fluency in English (verbal and written). Nice to Have: Hands-on experience orchestrating and monitoring stateful workflows using AWS Step Functions.
Practical experience using Duck DB for local, in-memory, or embedded analytical processing. #J-18808-Ljbffr

📌 Data Engineer (Ciudad de México)
🏢 Ensitech
📍 Ciudad de México