Senior Engineering Lead Analyst-4

Realign Llc; Los Angeles (CA); On-site; USD 160,000 - 210,000; Full time

Job summary

Realign Llc seeks an experienced Senior Engineering Lead Analyst to drive data platform modernization from on-prem to cloud across banking and insurance clients. You will architect scalable solutions using Snowflake and Databricks on AWS and Azure, and design AI/GenAI data value chains with LLMs and vector databases.

The role requires deep knowledge of data governance, data mesh and Medallion architecture, plus hands-on Python and cloud ML pipelines.

Qualifications

• 15+ years of experience in data architecture, data engineering, and analytics platforms
• Strong consulting experience in large BFSI transformation programs
• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)
• Design and implement AI and Gen AI solution for data value chain
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

Responsibilities

• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure
• Design data integration pipelines (batch, real-time, big data) and analytics platforms
• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.
• Act as a trusted advisor to senior business and IT stakeholders
• Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).

Skills

Data architecture

Data mesh

Medallion Architecture

LLMs

Python

Cloud platforms

MLOps

Education

Bachelor's degree in CS or related field

Tools

Snowflake

Databricks

LangChain

AutoGen

Pinecone

Weaviate

Job description

Job Title: Senior Engineering Lead Analyst
Location: Los Angeles, CA / Dallas, TX / Chicago, IL (Onsite)Fulltime

Job Description

Must Have Technical/Functional Skills

• Architect enterprise data platforms for data lake, Lakehouse, streaming systems.
• Design data integration and data pipeline patterns
• Should be able to evaluate new technologies and run proof of concepts.
• Should be able to set data and AI strategy for data organization.
• Established data Quality, lineage and metadata standards
• Ensured compliance with privacy, security and regulation
• Drives adoption of responsible AI frameworks
• Created architectural guardrails
• Drive consensus on standards (eg data contracts, lineage) across different data organizations
• Reviews design and elevate architectural thinking across teams
• Creates reusable patterns, templates and reference architectures
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Design and implement AI and Gen AI solution for data value chain
• Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
• Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.
• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
• Experience with A2A orchestration, agent memory strategies, and tool calling.
• Strong grasp of enterprise architecture, data governance, and security protocols.
• Experience with cloud platforms (Azure, AWS, GCP) and MLOps pipelines.
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure
• Design and implement AI and Gen AI solution for data value chain
• Design data integration pipelines (batch, real-time, big data) and analytics platforms
• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.
• Act as a trusted advisor to senior business and IT stakeholders
• Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.
• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

Generic Managerial Skills, If any

• 15-20 years of experience in data architecture, data engineering, and analytics platforms
• Strong consulting experience in large BFSI transformation programs
• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)
• Design and implement AI and Gen AI solution for data value chain
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Experience with cloud data services in aws,azure,gcp
• Strong background in data integration, reporting, and big data ecosystems
• Experience working in regulated environments with data governance and compliance requirements
• Excellent stakeholder communication and leadership skills

Required Skills

Job Type: Full Time

Job Category: IT

Job Description

Job Title: Senior Engineering Lead Analyst
Location: Los Angeles, CA / Dallas, TX / Chicago, IL (Onsite)Fulltime

Senior Cloud Data & AI Architect

Must Have Technical/Functional Skills

• Architect enterprise data platforms for data lake, Lakehouse, streaming systems.
• Design data integration and data pipeline patterns
• Should be able to evaluate new technologies and run proof of concepts.
• Should be able to set data and AI strategy for data organization.
• Established data Quality, lineage and metadata standards
• Ensured compliance with privacy, security and regulation
• Drives adoption of responsible AI frameworks
• Created architectural guardrails
• Drive consensus on standards (eg data contracts, lineage) across different data organizations
• Reviews design and elevate architectural thinking across teams
• Creates reusable patterns, templates and reference architectures
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Design and implement AI and Gen AI solution for data value chain
• Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).
• Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.
• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
• Experience with A2A orchestration, agent memory strategies, and tool calling.
• Strong grasp of enterprise architecture, data governance, and security protocols.
• Experience with cloud platforms (Azure, AWS, GCP) and MLOps pipelines.
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).
• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure
• Design and implement AI and Gen AI solution for data value chain
• Design data integration pipelines (batch, real-time, big data) and analytics platforms
• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.
• Act as a trusted advisor to senior business and IT stakeholders
• Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.
• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

Roles & Responsibilities

• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure
• Design and implement AI and Gen AI solution for data value chain
• Design data integration pipelines (batch, real-time, big data) and analytics platforms
• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.
• Act as a trusted advisor to senior business and IT stakeholders
• Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).
• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.
• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.
• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

Generic Managerial Skills, If any

• 15-20 years of experience in data architecture, data engineering, and analytics platforms
• Strong consulting experience in large BFSI transformation programs
• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)
• Design and implement AI and Gen AI solution for data value chain
• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation
• Experience with cloud data services in aws,azure,gcp
• Strong background in data integration, reporting, and big data ecosystems
• Experience working in regulated environments with data governance and compliance requirements
• Excellent stakeholder communication and leadership skills

Required Skills

Data Analyst