Database Administrator

BRILLIENT CORPORATION•The Woodlands, TX

45d•Hybrid

About The Position

Brillient Corporation is seeking a Data Scientist / ETL Engineer to support a mission-critical IRS data warehousing initiative. The role designs, builds, and maintains ETL processes that move data into the Compliance Data Warehouse (CDW), and applies statistical and machine learning techniques to produce predictive models and actionable insights. It works with data architects, business analysts, data quality specialists, and mission stakeholders to ensure data is accurate, consistent, and useful for analysis. The position supports CDW operations within RAAS, including data analysis, process improvement recommendations, and research and evaluation of emerging technologies to improve RAAS data availability, analytics, and value to stakeholders. The CDW is a non-IT data warehouse containing IRS return, entity, information return, regulatory, enforcement, web, and security data. The team performs work across ETL, database administration, SQL and Bash development, SAS administration, system security, COTS ETL development, metadata, data quality, customer service, web design, and intergovernmental data exchanges. The role extracts data from Unix/Linux systems and transforms and loads it into Sybase IQ and Postgres. It develops and refactors ETL jobs, scripts, stored procedures, data pipelines, and analytical workflows; analyzes complex datasets; develops and evaluates statistical and machine learning models; and works to optimize processing performance and data quality. It also documents processes and results, collaborates with stakeholders, participates in Scrum and client meetings, and works across Linux/Unix, Windows, containerized, and distributed environments.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Systems, or a related technical field.
US citizenship.
Ability to obtain and maintain a Public Trust security clearance.
At least eight years of combined professional experience in ETL development and data science, analytics, or machine learning.
Strong SQL ETL experience, focused on load, select, and update commands.
High proficiency in Linux/Unix, Sybase IQ, and Sybase SQL scripting.
Working experience with Postgres and Postgres SQL scripting.
Strong hands-on Python experience for ETL, data analysis, and machine learning, including Pandas, NumPy, and Jupyter Notebook.
Demonstrated experience building and evaluating machine learning models with scikit-learn or comparable frameworks.
Experience with data cleaning, transformation, exploratory data analysis, feature engineering, and model evaluation.
Shell scripting experience for automation and data processing, including KSH, Bash, SSH, sed, and awk.
Experience working in Linux and Windows environments.
Experience with OpenShift and/or Kubernetes.
Strong communication skills and the ability to work independently and collaboratively in a fast-paced environment.

Nice To Haves

Experience with AI, LLMs, Retrieval-Augmented Generation (RAG), or model fine-tuning.
Experience with Apache Airflow and automated ETL pipelines.
Experience with SAP Data Services or other COTS ETL tools.
Experience with Git/GitLab, DBeaver, VS Code, SecureCRT/SecureFX, Wiki, JSON, and Perl.
Experience deploying machine learning models in containerized or cloud environments.
Experience with MLOps, including model deployment, monitoring, and lifecycle management.

Responsibilities

Extract data and tables from Unix/Linux systems, transform them, and load them into Sybase IQ and Postgres.
Refactor existing ETL jobs and build new ETL solutions, including an object-oriented Unix-based framework of scripts and stored procedures to run and monitor ETL and statistics processes.
Develop and run shell, SQL, and Python scripts for data processing, automation, and analytical workflows.
Build repeatable and scalable data pipelines and analytical processes.
Troubleshoot shell and SQL errors and configure and operate SSH clients across servers.
Prepare, clean, transform, explore, and engineer features from data using Pandas and NumPy.
Analyze large, complex datasets and tables to identify trends, patterns, relationships, and actionable insights.
Develop, implement, evaluate, and maintain statistical and machine learning models using scikit-learn or comparable frameworks.
Validate model results, assess performance, and identify ways to improve model accuracy and reliability.
Conduct exploratory and statistical analysis to support data-driven decision-making.
Develop and document analytical workflows and models in Jupyter Notebook.
Work with structured and unstructured data from multiple sources and formats.
Optimize ETL processes, monitor jobs, and promptly resolve performance bottlenecks and failures.
Maintain data accuracy and integrity through validation, cleansing, and auditing; work with the Data Quality team to resolve inconsistencies and issues.
Collaborate with Data Architects on data models and schemas that meet business needs.
Translate requirements from analysts, SMEs, and stakeholders into ETL and analytical solutions.
Document ETL processes, workflows, data dictionaries, methodologies, models, assumptions, and results.
Help users move data into Sybase IQ and provide metadata for the metadata repository.
Present technical findings clearly to technical and non-technical audiences.
Participate in Scrum and client meetings and keep Kanban board cards up to date.
Work in Linux/Unix and Windows environments, including containerized and distributed platforms such as OpenShift and Kubernetes.
Keep up with emerging ETL, data science, machine learning, and AI technologies and recommend process and infrastructure improvements.