Urgent Need Senior Data Engineer - Financial Fraud Analytics

Hope you are doing well. We have an open position for a Senior Data Engineer. Pl. go through the below description and let me know your interest. If you are interested, kindly share a copy of your resume to ***email_hidden*** along with the rate / salary and best time to reach you.

Role: Senior Data Engineer (Need 2 Candidates)

Work Arrangement: Remote/telework, with onsite participation when requested

Client: Federal Government SBA Office of Inspector General

Position Summary

The Senior Data Engineer will design, implement, maintain, and improve an integrated and flexible data architecture within SBA OIG s Microsoft Azure environment.

The role will support audits, investigations, fraud analytics, and machine-learning activities by developing sustainable data pipelines, migrating source data, improving data quality, implementing source control, and maintaining reliable cloud-based data-processing environments.

Responsibilities

• Provide authoritative expertise in data-engineering methods and best practices.
• Apply code-first development approaches and modern pipeline-design patterns.
• Design and maintain a secure, stable, scalable, and flexible data architecture.
• Manage data assets through source control.
• Design, implement, and maintain ELT/ETL pipelines.
• Develop pipelines using Azure Synapse and Azure Machine Learning.
• Work with Azure Machine Learning SDK V1 and SDK V2.
• Migrate source data into Azure Data Lake Storage.
• Review, maintain, and improve existing architecture and pipelines.
• Conduct periodic reviews to identify bottlenecks, deprecated dependencies, and architecture drift.
• Implement pipeline quality controls, error handling, logging, monitoring, and validation checks.
• Incorporate source control into data pipelines and analytics codebases.
• Optimize data ingestion, processing, storage, and retrieval.
• Work with structured, semi-structured, and unstructured data.
• Use modern columnar formats, including Parquet.
• Normalize common entity attributes, including names, addresses, telephone numbers, and other identifying information.
• Develop self-service capabilities that allow SBA OIG analysts to query and export data.
• Coordinate with data scientists to support machine-learning models and analytical pipelines.
• Develop SOPs for authoring, developing, validating, publishing, executing, and monitoring pipelines and assets.
• Develop data dictionaries, entity-relationship diagrams, pipeline maps, and architecture documentation.
• Expand the environment with additional datasets and services as requested.
• Establish intake, testing, and production-deployment procedures.
• Monitor pipelines to ensure performance and regular dataset updates.
• Recommend architecture changes that reduce cloud costs.
• Evaluate emerging AI, automation, coding-assistant, and LLM-assisted data-engineering capabilities.

Required Qualifications

Candidates Must Possess One Of The Following

• Bachelor s degree in Data Engineering, Computer Science, Data Science, Machine Learning, Mathematics, or a related field; or
• Five years of applied work experience in one or more of these fields.

Candidates must have at least five years of hands-on experience in each of the following:

• Maintaining SQL databases.
• Conducting advanced SQL and T-SQL operations.
• Designing, implementing, and maintaining ELT/ETL processes in cloud-based data-analytics environments.

Candidates must have at least three years of hands-on experience in each of the following:

• Working with Azure Synapse.
• Working with Azure Machine Learning.
• Working with modern data-stack technologies.
• Manipulating data using Python.
• Using Pandas.

Preferred Qualifications

• Microsoft DP-203 certification or equivalent.
• PySpark or Polars experience.
• Experience developing reusable and modular code.
• Experience implementing pipelines and infrastructure using Python SDKs, command-line tools, REST APIs, or Infrastructure-as-Code tools.
• Experience implementing source-control and CI/CD workflows.
• Familiarity with AI coding assistants and LLM integration patterns.