Data Engineer Lead

Blend360

23h

About The Position

Blend is seeking a Lead Data Engineer to support the design, development, and optimization of data solutions for a high-impact enterprise initiative. The role focuses on preparing and enabling Snowflake data for a Dataiku PO agent proof of concept (POC). The successful candidate will ensure data is ingested, transformed, standardized, and structured to support the POC and downstream analytical workflows. The Lead Data Engineer will collaborate with technical and business stakeholders to deliver reliable, high-quality datasets and scalable data pipelines. The work includes data modeling, ingestion and ETL workflows, data quality validation, and technical documentation.

Requirements

At least 5 years of experience in Data Engineering or Data Analytics roles with a strong engineering focus.
Strong hands-on experience with Snowflake.
Strong SQL skills, including data transformation, querying, and validation.
Experience with data modeling and preparing datasets for analytical and downstream applications.
Experience implementing data quality checks to ensure accuracy, consistency, and completeness.
Experience building and maintaining ETL pipelines and data ingestion workflows.
Hands-on experience with Python for data processing and transformation.
Strong attention to detail and focus on data reliability and quality.
Ability to collaborate effectively with technical and business stakeholders.

Nice To Haves

Experience integrating or connecting Snowflake with Dataiku.
Familiarity with AI-driven or agent-based proof-of-concept initiatives.

Responsibilities

Prepare, structure, and enable Snowflake data for the Dataiku PO agent POC.
Design, develop, and maintain data pipelines to ingest, transform, normalize, and prepare data from multiple sources.
Design and implement scalable, reliable, and maintainable data ingestion workflows in Snowflake.
Develop and maintain data models for analytical use cases and POC requirements.
Implement data quality checks and validation rules for accuracy, consistency, completeness, and reliability.
Use SQL and Python to process, transform, and validate large datasets.
Identify and resolve data inconsistencies, anomalies, and structural issues affecting downstream consumption.
Support ETL processes and file-handling workflows for structured and semi-structured data.
Collaborate with business and technical stakeholders to translate data requirements into reliable data solutions.
Contribute to technical documentation and improve data processes, standards, and validation practices.

Benefits

Certifications in AWS, Databricks, and Snowflake.
Access to AI learning paths, study plans, courses, and role-tailored certifications.
Access to Udemy Business courses.
English lessons.
Travel opportunities to attend industry conferences and meet clients.
Career development plans and mentorship programs.
Special day rewards for birthdays, work anniversaries, and other personal milestones.
Company-provided equipment.
Flexible working options.
Other benefits may vary by location in LATAM.