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.