Senior Data Engineer

Ulta Beauty, Inc.•Bolingbrook, IL

19d

About The Position

The Senior Data Engineer is responsible for supporting, planning and coordinating functional and technical activities related to data engineering in a highly dynamic and fast paced environment. They work alongside engineers, analysts and developers to deliver scalable robust innovative technical solutions. This position plays a key role in building batch and real-time data pipelines, streaming analytics and developing machine learning and agentic AI solutions on our GCP and Databricks platforms to enhance Ulta guest experience.

Requirements

Bachelor’s degree in computer science, a related field, or applicable work experience.
5 years of development experience on various GCP services, Databricks (GCP preferred) and exposure to AI agentic development capabilities.
In-depth understanding large data warehouse ecosystems underlying architectures to re-platform legacy systems and build integration solutions on Google Cloud platform and Databricks.
Experience in design and development of data pipelines leveraging batch and real-time data ingestion frameworks and spark streaming
Experience Have a passion for new technologies, continuous improvement and actively involved with the team to design new data solutions, monitoring and alerting policies for data solutions.
Expertise in Java, Python or any scripting language, SQL/NoSQL Database systems.
Experience in tools and technologies such as BigQuery, Databricks, Spark, Kafka Streams
Experience in test automation ensuring data quality, continuous integrations build and deployment processes using Docker, GitHub, Jenkins and Unix/Linux shell scripts.
Hands-on experience developing complex services/solutions based on Enterprise Integration Patterns and Coding best practices.
Proactive and able to catch issues before failures.
Possess a strong work ethic; takes pride in producing a quality product and a strong team player
Work with production support and project consultants in an onshore / offshore model
Support off-hours production issues and code deployments as needed

Nice To Haves

Minimal travel required (training/conferences)

Responsibilities

Design, develop, and support scalable batch and real-time data pipelines for large-scale data processing across enterprise platforms like Google Cloud Platform and Databricks.
Build data engineering solutions on GCP using services such as BigQuery, Dataflow, Cloud Composer/Airflow, GCS, Pub/Sub, APIs, and related cloud services.
Develop and support data ingestion, curation, transformation, and consumption pipelines on Databricks using Spark, Delta Lake, notebooks, workflows/jobs, and Databricks SQL where applicable.
Optimize SQL, Spark, BigQuery, and Databricks workloads for performance, reliability, scalability, and cost efficiency.
Support automated deployment, code promotion, version control, and continuous integration practices using tools such as GitHub/Bitbucket, Jenkins, Docker, shell scripting, and other DevOps tools.
Collaborates with cross-functional technologists ad business partners across the organization to gather requirements, solve new problems and deliver quality results.
Mentor junior engineers and consultants on data engineering best practices, coding standards, cloud development, data quality, and operational excellence.
Supports the operations of the deployed solutions, investigates complex issues and assists with the resolution and implementation of preventive measures.
Assists with evaluation of Proof of Concepts (POCs), project estimation and engages in performance testing and tuning activities for large data sets.