Job Title: Lead Data Engineer
Location: The Woodlands, TX
Type: Direct Hire W2 / Corp-Corp
Primary Skills : Data Engineer, Azure, Wisdom.ai
Exp : Min 12 Years of experience
Share your resume to satti.p...@yash.com
Job
Description:
Lead Data
Engineer & Analyst who can design, build, and operationalize modern data
platforms while enabling AI-driven analytics and business insights using Azure
data services and Wisdom.ai. This role bridges data engineering (pipelines,
modeling, architecture), analytics (insights, dashboards, semantic layers), and
AI enablement (natural language querying, agentic workflows). You will play a
critical role in turning raw data (payments, POS, customer, operational) into
revenue-generating intelligence and decision support tools.
Key
Responsibilities:
- Data Engineering & Platform Development : Oversee the design and build of scalable data pipelines using Azure
Data Factory, Synapse Pipelines, and Fabric Dataflows. Over the develop
and maintenance of data lakes (Azure Data Lake Storage Gen2) and data
warehouses (Azure Synapse / Fabric Warehouse). Oversee the Implementation
of batch and near real-time ingestion using Event Hub, APIs, and file
drops. Ensure data quality, lineage, and observability.
- Data Modeling:
Design business-friendly data models including
star and snowflake schemas and domain-driven models (merchant,
transaction, customer, device). Define metrics, KPIs, and standardized
definitions such as authorization rate and interchange yield.
- Analytics & Business Insights: Enable self-service analytics
for product, operations, risk, and finance teams. Perform deep-dive
analyses on payment performance (authorization rates, declines, routing),
customer behavior and segmentation, and revenue optimization
opportunities.
- Wisdom.ai Enablement (AI + Natural Language Analytics): Integrate and configure Wisdom.ai with Azure data sources. Build
semantic knowledge layers for natural language querying, including
business context, definitions, relationships, and hierarchies. Enable “ask
your data” capabilities for non-technical users and develop AI-driven insights
and recommendations (agentic analytics).
- Data Governance & Compliance: Implement governance aligned
with PCI, SOC2, and enterprise data policies. Define data ownership,
access controls, and RBAC. Ensure secure handling of sensitive data (PII,
payment data). Maintain metadata catalogs and documentation.
- Cross-Functional Collaboration: Partner
with product managers (feature analytics, KPIs), engineering teams (data
integration), and business stakeholders (requirements, insights).
Translate business needs into data models, pipelines, and dashboards.
Thank You
Satti Reddy