Data Engineer (Mid-Level) — AI & Data Pipelines

SRI; Irving (TX); On-site; USD 83,000 - 165,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

401(k) with company match

Short-term disability

Life insurance with AD&D

Job summary

SRI Resources is seeking a mid-level Data Engineer to join our Enterprise AI team for a 12+ month contract on-site in Irving, TX. You will build scalable data pipelines, integrate enterprise data with AI outputs, and collaborate with data scientists and product partners to deliver reliable data solutions.

The role focuses on developing data models, pipelines, and interfaces for AI-enabled workflows, with emphasis on data quality, monitoring, and scalable processing in a production environment.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Engineering, Mathematics, Statistics, or a related quantitative discipline, or equivalent practical experience.
• 3–5 years of relevant experience in data engineering or a closely related software engineering role.
• Strong hands‑on proficiency in Python and SQL, including complex queries, transformations, and troubleshooting.
• Experience with Spark/PySpark and distributed data processing.
• Experience building and supporting ETL/ELT pipelines on a cloud platform such as Azure or AWS.
• Understanding of data modeling, relational databases, data lakes, data warehouses, and incremental processing patterns.
• Experience with workflow orchestration tools such as Apache Airflow or Databricks Workflows.
• Familiarity with event-driven or streaming data processing concepts, including event time, deduplication, and reliable delivery.
• Experience with Git, automated testing, and production monitoring or troubleshooting.
• Ability to independently deliver scoped work, communicate technical concepts clearly, and collaborate with technical and business stakeholders.

Responsibilities

• Build, maintain, and enhance scalable batch and near-real‑time data pipelines using Python, SQL, and Spark/PySpark.
• Integrate data from enterprise applications, operational systems, APIs, event streams, and AI inference outputs into reliable, analysis-ready datasets.
• Develop transformations that combine structured and semi-structured data across sources using consistent identifiers and timestamps; handle time zones, duplicate records, and late-arriving events.
• Create and maintain data models and curated datasets for business analytics, AI model development and evaluation, and AI-enabled application workflows.
• Partner with AI/ML engineers to deliver data interfaces for inference workflows and capture model outputs, confidence scores, and user feedback for analysis and continuous improvement.
• Implement data quality checks, reconciliation, pipeline monitoring, and alerting to ensure completeness, accuracy, freshness, and traceability.
• Troubleshoot pipeline failures and performance issues; implement reliable retries, backfills, and incremental processing while optimizing compute and storage costs.
• Collaborate with product managers, data scientists, analysts, and engineering partners to translate business requirements into practical data solutions.
• Follow data security, privacy, access‑control, and retention requirements when handling enterprise data and AI-generated outputs.
• Write maintainable, tested code; participate in code reviews and CI/CD practices; document schemas, data lineage, dependencies, and operational runbooks.

Skills

Python

SQL

Spark/PySpark

ETL/ELT pipelines

Azure

AWS

Data modeling

Airflow/Databricks Workflows

Git

CI/CD

Communication

Education

Bachelor’s degree or equivalent in a quantitative field

Tools

Airflow

Databricks Workflows

Git

Job description

SRI Resources is seeking a mid-level Data Engineer to join our Enterprise AI team for a 12+ month contract on-site in Irving, TX. You will build scalable data pipelines, integrate enterprise data with AI outputs, and collaborate with data scientists and product partners to deliver reliable data solutions.

The role focuses on developing data models, pipelines, and interfaces for AI-enabled workflows, with emphasis on data quality, monitoring, and scalable processing in a production environment.