Senior Data Engineer | Remote LATAM Only

Agentic Dream; Town of Poland (NY); Remote; USD 120,000 - 150,000; Full time

Job summary

A global data solutions company in New York is seeking a Senior Data Engineer to design and maintain robust ETL/ELT pipelines. Ideal candidates should have over 7 years of experience in data engineering, strong proficiency in SQL and Python, and familiarity with cloud platforms like Azure. This role involves data integration, standardization, and documentation to enhance data governance and support BI dashboards. Competitive salary and engaging challenges await.

Qualifications

• 7+ years of experience in enterprise-scale data engineering.
• Strong proficiency in SQL and Python.
• Experience with cloud platforms like Azure.
• Demonstrated experience with data modeling best practices.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines.
• Implement bronze, silver, and gold layers in data lakes.
• Align global units of measure across products.
• Document pipeline architectures and data transformation logic.

Skills

SQL (Advanced)

Python for Data Processing

Spark or Databricks

Cloud platforms (Azure Data Lake/Blob, Synapse)

ETL orchestration tools (Azure Data Factory, Airflow, dbt)

API integrations

Master data frameworks

Data modeling best practices

Fluent English (C1)

Interpersonal skills

Job description

At Agentic Dream, we're accelerating the transformation of global data landscapes. We're seeking highly experienced Senior Data Engineers to help deliver a robust and scalable data architecture across our global ERP systems.

If you're passionate about building end-to-end pipelines, enabling AI/BI solutions, and thriving in fast-paced, high-stakes environments, this is your challenge.

Learn more about us at: https://www.agenticdream.com/solutions

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 (e.g., NetSuite, QuickBooks, Salesforce, RF Smart, etc.).

Demonstrated experience with:

• Master data frameworks, unit conversion, and ERP-to-warehouse mapping.
• Handling both structured and unstructured data.
• Data modeling best practices (star schema, snowflake schema, etc.).

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 Microsoft Teams/email.
• Exposure to AI/ML pipelines, including data preparation for machine learning models or anomaly detection systems.

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/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 Power 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

• Work closely with the Data Architect Lead 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 clearly.
• Ensure adherence to data governance policies and assist with metadata management.