Lead Data Engineer

Established in 2011, Trinetix is a dynamic tech service provider supporting enterprise clients around the world. Headquartered in Nashville, Tennessee, the company has a global team of over 1,000 professionals and delivery centers across Europe, the United States, and Argentina. They partner with leading global brands, delivering innovative digital solutions across Fintech, Professional Services, Logistics, Healthcare, and Agriculture.

• 5+ years of experience in data engineering, analytics engineering, or related fields
• Strong SQL expertise and experience designing complex data transformations
• Hands-on experience with modern data platforms (e.g., GCP BigQuery, Databricks, Snowflake)
• Experience with ELT or ETL, orchestration such as Airflow or dbt, Spark, data warehouses and lakehouses such as Snowflake, BigQuery, or Databricks, streaming such as Kafka or Kinesis, metadata and data quality
• Proficiency in Python
• Strong understanding of data modeling concepts and data structuring for analytics
• Experience working with stakeholders to define requirements and deliver data solutions
• Ability to balance hands-on delivery with technical guidance

Nice to have:

• Familiarity with data governance, lineage, or metadata management concepts
• Experience working with cross-domain data (e.g., customer, product, transactions)
• Exposure to financial services or other data-intensive industries
• Experience supporting migration or evolution of data platforms (e.g., toward Databricks)
• Experience with orchestration tools (e.g., Airflow, Cloud Composer)
• Exposure to data modeling concepts (e.g., dimensional models, data marts, reusable datasets)

• Lead design and implementation of data pipelines, transformations, and curated datasets
• Translate business requirements into structured data solutions and models
• Guide and execute development of reusable, analytics-ready data assets
• Ensure consistency in key data elements definitions across systems
• Collaborate with stakeholders to define and refine data requirements and logic
• Review and guide engineering work to ensure quality, performance, and scalability
• Establish best practices for data engineering, transformation, and data quality
• Support integration of data across multiple domains and systems
• Contribute to architecture decisions across data platforms and tooling
• Mentor and support other engineers on the team

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