Senior Data Engineer / Data Scientist

Brillient Corporation is hiring a Senior Data Engineer / Data Scientist to support an IRS data warehousing initiative. The role combines ETL pipeline development and maintenance with statistical analysis, machine learning, and analytical solutions for the Compliance Data Warehouse.

Responsibilities:

• Extract data and tables from Unix/Linux systems and transform and load them into Sybase IQ and PostgreSQL
• Refactor existing ETL jobs and develop new solutions, including object-oriented, Unix-based frameworks of scripts and stored procedures to execute and monitor ETL and statistical processes
• Develop and execute shell, SQL, and Python scripts to support data processing, automation, and analytical workflows
• Build repeatable, scalable data pipelines and analytical processes
• Troubleshoot shell and SQL errors and configure and operate SSH clients across servers
• Prepare, clean, transform, and explore data, including feature engineering using Pandas and NumPy
• Analyze large, complex datasets 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 improve accuracy and reliability
• Conduct exploratory and statistical analysis to support data-driven decision-making
• Develop and document analytical workflows and models using Jupyter Notebook
• Work with structured and unstructured data from multiple sources and formats
• Optimize ETL processes to ensure efficient and timely data processing
• Monitor ETL jobs and resolve performance bottlenecks and failures
• Maintain data accuracy and integrity through validation, cleansing, and auditing
• Collaborate with the Data Quality team to identify and resolve data inconsistencies
• Partner with Data Architects to design data models and schemas that meet business requirements
• Translate business and mission requirements from analysts, subject matter experts, and stakeholders into ETL and analytical solutions
• Document ETL processes, workflows, data dictionaries, methodologies, models, assumptions, and results
• Support users in loading data into Sybase IQ and providing metadata for the metadata repository
• Communicate complex technical findings to both technical and non-technical audiences
• Participate in Scrum and client meetings and maintain current Kanban board activities
• Work across Linux/Unix and Windows environments, including containerized and distributed platforms such as OpenShift and Kubernetes
• Evaluate emerging ETL, data science, machine learning, and AI technologies and recommend improvements to processes and infrastructure

Requirements:

• U.S. Citizenship Required
• Must be able to obtain and maintain a Public Trust determination
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Systems, or a related technical field
• Minimum of eight (8) years of combined professional experience in ETL development and data science, analytics, or machine learning
• Strong SQL and ETL experience, including load, select, and update commands
• High proficiency in Linux/Unix, Sybase IQ, and Sybase SQL scripting
• Working experience with PostgreSQL and PostgreSQL scripting
• Strong hands-on Python experience supporting ETL, data analysis, and machine learning, including Pandas, NumPy, and Jupyter Notebook
• Demonstrated experience developing and evaluating machine learning models using scikit-learn or comparable frameworks
• Experience with data cleaning, transformation, exploratory data analysis (EDA), feature engineering, and model evaluation
• Experience with shell scripting and automation, including KSH, Bash, SSH, sed, and awk
• Experience working in Linux/Unix and Windows environments
• Experience with OpenShift and/or Kubernetes
• Strong analytical, problem-solving, and communication skills, with the ability to work independently and collaboratively in a fast-paced environment
• U.S. citizenship is required
• Experience with artificial intelligence (AI), large language models (LLMs), Retrieval-Augmented Generation (RAG), or model fine-tuning
• Experience with Apache Airflow and automated ETL pipelines
• Experience with SAP Data Services or other commercial off-the-shelf (COTS) ETL tools
• Experience with Git/GitLab, DBeaver, VS Code, SecureCRT/SecureFX, Wiki platforms, JSON, and Perl
• Experience deploying machine learning models in containerized or cloud environments
• Experience with MLOps, including model deployment, monitoring, and lifecycle management