Senior Data Engineer

Agile Resources, Inc.; United States; On-site; USD 82,656 - 96,432; Full time

Job summary

Agile Resources, Inc. is seeking a Sr. Data Engineer for a 12-month contract to lead the development of a next-generation data ecosystem. This role demands expertise in architecting high-throughput, distributed computing frameworks and building robust data pipelines for machine learning applications.

The ideal candidate will have over 7 years of experience, profound knowledge in Spark tuning, and hands-on experience in Python and SQL. If you excel in solving complex data challenges and driving technical ownership, this role is your next career move.

Qualifications

• 7+ years of data engineering experience with mastery in Spark tuning and partitioning.
• Hands-on experience in designing secure, ACID-compliant storage layers.
• Expertise in Python and SQL with ML lifecycle management experience.

Responsibilities

• Architect and optimize high-throughput distributed computing frameworks.
• Construct production-grade features for LLM and predictive ML models.
• Build automated ingestion frameworks across multi-cloud environments.

Skills

Spark tuning

Cloud infrastructure

Python

SQL

ACID-compliant storage layers

Event-driven architectures (Kafka)

Tools

Databricks

MLflow

Delta Lake

Job description

Type: 12-month Contract with Potential to Extend or Convert

Compensation: $60/hr - $70/hr

Applicants must be legally authorized to work in the United States on a full-time basis. We are unable to provide visa sponsorship for this position now or in the future.

We are partnering with an industry leader in the construction supplies and equipment space to find a Sr. Data Engineer to spearhead the evolution of their global, next-generation data ecosystem. If you thrive on solving intricate, massive-scale data puzzles, optimizing bleeding-edge distributed computing environments, and laying the groundwork for sophisticated machine learning and LLM operations, this is your next definitive career move.

In this role, you will act as the architect behind a mission-critical platform, transforming raw datasets into powerful, secure, and production-ready intelligence that fuels executive-level decisions. You will enjoy a high degree of technical ownership, bridging the gap between advanced cloud engineering and real-world AI applications.

Here’s What You’ll Be Doing

• Architect and optimize high-throughput, distributed computing frameworks and modern Lakehouse structures to handle massive data velocity and volume.
• Construct robust, production-grade features and pipelines that seamlessly operationalize large language models (LLMs) and predictive machine learning models.
• Build automated ingestion frameworks across multi-cloud environments, utilizing both real-time streaming and scheduled batch processing.
• Establish solid data quality, unified cataloging, and access controls while aggressively optimizing cluster performance and cloud infrastructure costs.
• Collaborate with cross-functional leadership, finance, and operations teams to convert complex strategic goals into highly scalable technical solutions.

Here’s What Our Ideal Candidate Has

• 7+ years of sophisticated data engineering experience, highlighted by mastery-level knowledge of Spark tuning, partitioning, and cloud infrastructure.
• Extensive hands-on experience designing secure, ACID-compliant storage layers, ideally utilizing modern unified cataloging tools.
• Expertise in Python and SQL, coupled with practical experience supporting ML lifecycle management (such as tracking, feature stores, or LLM integrations).
• A proven track record of integrating disparate, complex data sources and maintaining high-availability production environments at enterprise-scale.
• Familiarity with event-driven architectures (like Kafka), continuous integration/continuous deployment (CI/CD) pipelines, and infrastructure-as-code will be a plus.

Keywords: Lead Data Engineer, Senior Data Engineer, Databricks, Apache Spark, Delta Lake, Lakehouse Architecture, Python, SQL, Cloud Data Architect, MLOps, Data Pipelines, Spark Tuning, Unity Catalog, Kafka, Event Hubs, MLflow, LLM Infrastructure, Data Governance