Data Engineer

Ensitech; México; On-site; MXN 900,000 - 1,200,000; Full time

Job summary

Ensitech is seeking an experienced Data Engineer specialized in data integration and pipelines to build reliable, scalable data architectures. You will craft production-grade pipelines, ensure resilience, and integrate data across ERP/CRM/HR systems into cloud data warehouses and lakehouses.

The role emphasizes robust ETL/ELT workflows in Python/SQL, with attention to idempotence, retry logic, and end-to-end monitoring using AWS Step Functions.

Qualifications

• 5+ years of experience in data engineering.
• Experience integrating third-party enterprise platforms (ERP/CRM/HR).
• Proficient in Python and SQL with strong data modeling skills.

Responsibilities

• Design and maintain resilient data pipelines ingesting data from ERPs, CRMs, and HR platforms.
• Ingest, transform, and model high-volume data in Snowflake, Databricks, and S3.
• Enforce non-functional requirements: latency targets, rate limits, retries, and data integrity checks.
• Build resumable ingestion processes that recover from network or system failures without full restarts.
• Ensure idempotent data operations and robust retry mechanisms across pipelines.

Skills

Python

SQL

English proficiency

Data integration

Education

Bachelor’s degree in a technical field

Tools

Snowflake

Databricks

AWS S3

AWS Step Functions

DuckDB

Job description

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, NetSuite, 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 DuckDB for local, in-memory, or embedded analytical processing.