We are looking for highly experienced Senior Data Engineers to help deliver a robust and scalable data architecture across global ERP systems.
This role is ideal for someone passionate about building end-to-end pipelines, enabling AI/BI solutions, and thriving in fast-paced, high-stakes environments.
You will contribute to the acceleration of global data transformation initiatives, ensuring modern, enterprise-ready data ecosystems.
Key Responsibilities
Data Integration & Pipeline Development
- Design, build, and maintain scalable ETL/ELT pipelines from diverse ERP sources into centralized Data Lakes and Warehouses.
- Develop connectors for structured and semi-structured data using Python, SQL, APIs, or middleware solutions.
Data Lake & Warehouse Engineering
- Implement bronze, silver, and gold layers for ingestion, cleaning, and curated datasets.
- Organize data structures for optimized use in BI and AI systems.
Data Standardization & Cleansing
- Align global units of measure (lbs, kg, packaging, linear feet) across products and regions.
- Execute data deduplication, enrichment, and harmonization from disparate systems.
Architectural Collaboration
- Collaborate closely with Data Architecture leadership on schema definitions, partitioning strategies, and infrastructure design.
- Set up and maintain sandbox/staging environments for safe testing.
Power BI & AI Enablement
- Provide ready-to-use, clean datasets to support BI dashboards and AI/ML use cases.
Documentation & Governance
- Document pipeline architectures, data transformation logic, and integration points.
- Ensure adherence to data governance policies and support metadata management.
Requirements
Technical Requirements
- 7+ years of experience in enterprise-scale data engineering.
- Strong proficiency in:
- SQL (Advanced) and Python for data processing
- Spark or Databricks for distributed data workflows
- Cloud platforms such as Azure Data Lake/Blob, Synapse, or equivalents
- ETL orchestration tools like Azure Data Factory (ADF), Airflow, or dbt
- API integrations and data ingestion from ERP systems (NetSuite, QuickBooks, Salesforce, RF Smart, etc.)
- Demonstrated experience with:
- Master data frameworks, unit conversion, and ERP-to-warehouse mapping
- Handling structured and unstructured data
- Data modeling best practices (star schema, snowflake schema)
Soft Skills & Work Commitment
- Fluent English (C1 level) – required for daily client calls and clear technical documentation.
- Strong interpersonal and collaboration skills to work with cross-functional teams (BI, QA, DevOps, Business Analysts).
Nice to Have
- Experience standardizing data across global manufacturing or supply chain environments.
- Familiarity with Power BI datasets, alert triggers, and integration with messaging/email tools.
- Exposure to AI/ML pipelines, including data preparation for machine learning or anomaly detection.
Skills Required
- 7+ years of experience in enterprise-scale data engineering
- Advanced SQL proficiency
- Python for data processing
- Experience with Spark or Databricks
- Experience with cloud platforms such as Azure Data Lake, Azure Blob, or Synapse
- Experience with ETL orchestration tools such as Azure Data Factory, Airflow, or dbt
- Experience with API integrations and ERP data ingestion
- Experience with master data frameworks, unit conversion, and ERP-to-warehouse mapping
- Experience handling structured and unstructured data
- Knowledge of star schema and snowflake schema data modeling
- Fluent English at C1 level
- Strong interpersonal and collaboration skills
- Experience standardizing data across global manufacturing or supply chain environments
- Familiarity with Power BI datasets, alert triggers, and messaging or email integrations
- Exposure to AI/ML pipelines, machine learning data preparation, or anomaly detection
What We Do
Agentic Dream helps enterprises become AI-native by embedding forward-deployed AI engineering pods of senior engineers—based in Latin America—directly into client businesses. It offers AI readiness assessments, AI engineering, agents and automation, and data/RAG pods, plus an AI-Native Delivery Factory operating system, to design, build, and scale AI-powered systems and agents for enterprise workflows in fintech, healthcare, retail, and other industries.
