Senior Engineering Lead Analyst-3

REALIGN LLC; Los Angeles (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

REALIGN LLC is seeking a Senior Engineering Lead Analyst to drive enterprise data platform modernization and AI-driven data value chains. You will architect scalable pipelines across batch and real-time data, leverage Snowflake and Databricks on cloud, and define governance, quality, metadata, and lineage standards.

Strong consulting experience in BFSI transformation is a plus. The role requires deep data architecture expertise, hands-on Python, and proficiency with LLMs, vector databases, and

Qualifications

• Strong background in enterprise data architecture and analytics platforms.
• Experience with data mesh, medallion architecture, and governance.
• Hands-on with Python and modern AI tooling for data value chains.
• Proficiency in cloud data services and MLOps pipelines.
• Ability to design scalable data integration and AI solutions.

Responsibilities

• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.
• Architect scalable solutions using Snowflake and Databricks on AWS/Azure.
• Design and implement AI and Gen AI solutions for the data value chain.
• Define and implement data governance, quality, metadata, and lineage frameworks.
• Act as trusted advisor to senior business and IT stakeholders.

Skills

Data architecture

Data governance

Python

LLMs & AI models

MLOps

Data mesh

Data contracts

Compliance

Education

Bachelor's degree in Computer Science

Tools

Snowflake

Databricks

Azure

AWS

GCP

LangChain

AutoGen

FAISS

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