Engineer I- IT Systems

Microchip•Chandler, AZ

21h•Onsite

About The Position

We are seeking an early-career Business Systems Engineer with a strong foundation in Data Pipeline Management, supported by hands-on experience with dbt Core, SQL, and Databricks on AWS. This role is intended for candidates who already understand the fundamentals of building, deploying, and supporting Data Universe systems and want to apply those skills in a production data platform. You will work on Data Engineering workflows end-to-end, from preparing high-quality data to supporting Analytics which enabled business decisions making, with hands-on ownership.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field
0-3 of professional experience
Strong SQL skills with experience transforming analytical datasets. Understand how joins, aggregations, and window functions impact performance and correctness
Hands-on experience with Databricks on AWS (academic, internship, or project-based)
Working knowledge of dbt Core, including models, tests, and documentation
Familiarity with Python for data processing or ML workflows
Experience using Git or another version control system
Understand data governance concepts, including access control and lineage, within Unity Catalog
Good communication and interpersonal skills
Creativity and problem solving
Identifying continuous improvement opportunities
Study current practices, think outside the box, and foster creative analysis to complex problems
High learning agility and adapt quickly to priority changes
Be self-driven
Work independently and in a team to develop innovation

Responsibilities

Perform regular data validation and cleansing to ensure the accuracy, integrity, and reliability of datasets
Identify and resolve data pipeline failures (debug data anomalies and issues using SQL and dbt test results)
Build and maintain ETL/ELT processes to move data from various sources into data warehouses or lakes
Write and optimize SQL transformations that support feature engineering and model training
Setup data catalog, execute and monitor data and ML workloads using Databricks
On-board data product owners to Data Universe platform
Support AWS-based lakehouse architectures, primarily using Amazon S3
Setup IAM (Identity and Access Management) roles, permissions, and secure access patterns
Troubleshoot and optimize cloud-based AI and data workflows
Apply dbt tests and documentation to ensure data quality for AI consumption
Work with contracted, curated datasets while understanding upstream raw source tables and lineage
Execute code reviews and follow established dbt and SQL standards
Assist in building and maintaining training and inference workflows on Databricks
Prepare and validate feature datasets used by ML models, ensuring correctness, consistency, and timeliness
Support LLM-enabled use cases, such as: embedding generation, semantic search, retrieval-augmented generation (RAG)
Monitor model inputs and outputs for data quality issues and unexpected behavior
Understand how upstream data changes affect model performance, stability, and bias.