Sr. Technical Data Delivery Lead

Location: Philly, PA (Onsite-3days)

Client: Citius Tech/Seltos

Location: US (Local to Philadelphia or ability to relocate to Philly within 1-2 weeks if selected). The candidate should be able to go into the office at least 3 times a week Experience: 12–15 years

Domain: Healthcare Payer / Medicaid / Medicare

We are seeking a Senior (Sr.) Technical Data Delivery Lead to play a critical, hands-on

role within the Enterprise Data Office (EDO). This role is responsible for owning technical delivery execution, ensuring high-quality, secure, and compliant data solutions, and driving outcomes across complex enterprise data initiatives.

The Sr. Technical Delivery Lead will be deeply involved across the stack—from cloud data engineering and data modeling to AI-assisted delivery and agentic development governance. This role requires strong judgment, independence, and a go-getter mindset, with the ability to guide both developers and AI agents while maintaining architectural

integrity.

 Key Responsibilities

Technical Leadership s Hands-On Delivery

·        Act as the technical owner and execution lead for EDO data initiatives

·        Perform deep, hands-on code and design reviews across:

o    Data pipelines

o    Data models

o    Cloud services

o    Human-written and AI-generated code

·        Ensure solutions meet standards for:

o    Code quality and maintainability

o    Data model correctness and usability

o    Performance, scalability, and cost efficiency

o    Security, compliance, and auditability

·        Step in hands-on as needed to:

o    Debug complex pipeline or data issues

o    Refactor models or transformations

o    Lead complex architectural changes

Data Modeling s Data Architecture Oversight

·        Review, validate, and guide data modeling approaches across platforms, including:

o    Conceptual, logical, and physical data models

o    Analytical and reporting models

·        Ensure consistent, high-quality modeling of healthcare payer domains, including:

o    Claims

o    Member

o    Provider

o    Eligibility

·        Enforce best practices around:

o    Grain definition

o    Key design (business vs surrogate keys)

o    Slowly Changing Dimensions (SCDs)

o    Historical tracking and auditability

·        Ensure data models align with:

o    Enterprise architecture standards

o    Reporting and analytics needs

o    Downstream consumption (BI, data science, regulatory reporting)

AI-Assisted s Agentic Development Leadership

·        Provide leadership for teams leveraging agentic IDEs and AI-assisted SDLC models

·        Guide effective use of agentic IDEs for:

o    Complex data pipelines

o    Cross-module data model changes

o    Multi-service and cross-domain initiatives

·        Define architectural and data-modeling intent that autonomous agents can reliably follow

·        Break down features into agent-executable tasks while maintaining data integrity

·        Govern AI autonomy through:

o    Guardrails

o    Permissions

o    Mandatory human reviews

·        Supervise AI agents operating across:

o    Multi-service systems

o    Legacy data platform modernization

o    Large enterprise codebases and monorepos

Architecture, Governance s CI/CD Integration

·        Design architectures that support Azure-first, AI-augmented, and agentic workflows

·        Integrate data engineering and agentic workflows into CI/CD pipelines, ensuring:

o    Controlled execution

o    Traceability and lineage

o    Safe rollback and recovery

·        Establish quality gates for:

o    Data model changes

o    Schema evolution

o    AI-generated code

·        Evaluate the impact of agentic IDE adoption on:

o    SDLC

o    CI/CD

o    Data quality

o    Security posture

o    Technical debt

Delivery Ownership s Stakeholder Communication

·        Own end-to-end technical delivery, from design and modeling through deployment

·        Operate independently and proactively drive execution

·        Provide clear, concise, and timely updates on:

o    Progress

o    Risks

o    Dependencies

o    Mitigation plans

·        Translate complex technical and data modeling decisions into business-friendly language

Agile Execution s Collaboration

·        Actively participate in Agile ceremonies:

o    Sprint planning

o    Daily stand-ups

o    Reviews and retrospectives

·        Partner closely with Product, Architecture, Data Governance, Security, QA, and Platform teams

·        Ensure sprint commitments are achievable and consistently delivered

Required Skills s Experience Core Technical Expertise

·        10–15 years of experience in data engineering and hands-on technical delivery roles, preferably in Healthcare Payer / Medicaid environments

·        Extensive, Azure-first cloud experience, including solution architecture, implementation, and operations

·        Strong hands-on expertise with Azure data platform services, including:

o    Azure Data Lake Storage (ADLS Gen2)

o    Azure Databricks

o    Azure Data Factory (ADF)

o    Azure Synapse Analytics

o    Azure SQL / SQL Server

o    Azure Key Vault

o    Azure DevOps (Repos, Pipelines, Boards)

·        Strong proficiency in:

o    SQL (advanced querying, tuning, optimization)

o    Python and PySpark

o    ETL / ELT development

·        Strong data modeling experience, including:

o    Conceptual, logical, and physical modeling

o    Dimensional modeling (star/snowflake schemas)

o    Experience handling SCDs and historized data

·        Deep understanding of:

o    Cloud-native Data Lake / Lakehouse architectures

o    Batch and streaming processing patterns

o    Incremental processing and CDC

·        Proven experience reviewing and optimizing:

o    Data models

o    Transformation logic

o    Schema evolution strategies

·        Experience implementing cloud and data governance controls across Dev/Test/Prod

Agentic IDE s AI-Assisted SDLC Experience

·        Proven experience architecting and delivering systems using agentic IDEs

·        Ability to:

o    Define architectural and data-modeling intent that agents can follow

o    Break features into agent-executable tasks

o    Govern AI autonomy (guardrails, permissions, reviews)

o    Integrate agentic workflows into CI/CD pipelines

·        Strong understanding of:

o    Security implications of autonomous code execution

o    Compliance, auditability, and traceability

o    AI-assisted SDLC operating models

Healthcare Domain Expertise

·        Strong experience in Healthcare Payer / Medicaid data environments

·        Familiarity with:

o    Claims