Data Engineer: Scalable Pipelines & AI Data Platforms

Resolve Tech Solutions; Irving (TX); On-site; USD 110,000 - 150,000; Full time

Job summary

Resolve Tech Solutions seeks a data engineer to build scalable batch and near-real-time data pipelines. You will collaborate with AI/ML teams, translate business requirements into robust data solutions, and ensure data quality and lineage across cloud platforms.

The role centers on Python, SQL, Spark, and modern data lake architectures, with emphasis on production readiness, CI/CD, and cost-efficient processing in Azure or AWS.

Qualifications

• Bachelor-level degree or equivalent practical experience in a quantitative field.
• 3–5 years of data engineering or closely related software engineering experience.
• Strong Python and SQL proficiency with complex queries and debugging.
• Experience with Spark/PySpark and distributed data processing.

Responsibilities

• Build, maintain, and optimize scalable batch and near-real-time data pipelines using Python, SQL, Spark.
• Integrate data from enterprise systems, APIs, and event streams into analysis-ready datasets.
• Collaborate with AI/ML teams to deliver data interfaces for inference workflows and model outputs.
• Ensure data quality, lineage, and governance across pipelines and data stores.
• Participate in CI/CD, code reviews, and documentation of data schemas and runbooks.

Skills

Python

SQL

Spark/PySpark

ETL/ELT

Data modeling

Data quality

CI/CD

Git

Education

Bachelor's degree in Computer Science, Software Engineering, Information Systems, Engineering, Mathematics, Statistics, or related field

Tools

Databricks

Airflow

Delta Lake

Kafka

Azure

AWS

Spark Structured Streaming

Databricks Workflows

Job description

Resolve Tech Solutions seeks a data engineer to build scalable batch and near-real-time data pipelines. You will collaborate with AI/ML teams, translate business requirements into robust data solutions, and ensure data quality and lineage across cloud platforms.

The role centers on Python, SQL, Spark, and modern data lake architectures, with emphasis on production readiness, CI/CD, and cost-efficient processing in Azure or AWS.