Data Engineer - PP Copy Mandatory

Client & Engagement Details

• Agency Name: NYS Department of Financial Services (DFS).
• Position Title: IT Specialist (Senior Level).
• Location: 1 State Street, New York, NY 10004.
• Engagement Length: 30 Months.
• Target Start Date: April 16, 2026.
• Hours & Schedule: Part-time (37.5 hours per week), typically 9:00 am – 5:00 pm.
• Telecommuting: Up to 50% remote at the discretion of the hiring manager.
• Interview Format: Webcasting (Camera required).

Requested Qualifications (Point-Based)

Candidates must demonstrate the following experience in months:

• Data Domain: 60 months in Data Engineering, Architecture, Data Governance, or Data Quality Assurance for Financial Reporting.
• SQL/RDBMS: 60 months in SQL and Relational Database Management Systems, including complex dataset querying.
• Financial Data: 60 months compiling, computing, and validating financial data using advanced statistical formulas.
• Data Pipelines: 36 months building/maintaining data workflows and pipelines, translating business needs into technical specs.
• Automation: 36 months automating data ingestion/reporting using Python, R, or Excel VBA.
• Visualization: 36 months utilizing PowerBI or Tableau.
• Cloud Platforms: 24 months in cloud-based platforms, preferably Snowflake.
• Governance Tools: 12 months with MDM/Cataloging platforms (e.g., Informatica, Collibra) for profiling and metadata management (Preferred).
• Education: Bachelor’s Degree in a quantitative discipline (CS, Statistics, Finance, etc.).

Day-to-Day Tasks

The selected candidate will perform the following duties:

• Technical Translation: Translate complex business requirements from Insurance stakeholders into technical specifications for pipelines and workflows.
• Workflow Engineering: Build and maintain data warehousing workflows and pipelines using SQL and cloud tools.
• Data Validation: Compile and validate insurance financial data using advanced statistical models.
• Requirements Management: Elaborate high-level policies into technical requirements, such as user stories and system use cases.
• Governance & Profiling: Execute data profiling, build exception reports, and manage metadata and lineage via governance platforms.
• Process Automation: Use Python, VBA, or R to automate data ingestion and reporting processes.
• Knowledge Transfer: Document all work via process flows, well-commented code, and data dictionaries for the team.