Simpalm’s client, a US-based omnichannel Insurtech organization, is hiring a Lead Data Engineer in Dallas, TX (onsite). The role focuses on designing and delivering enterprise-scale, AI-ready data platform capabilities, including ETL/ELT, orchestration, and support for AI workloads such as LLMs and RAG.

Role Focus

Own the end-to-end design, development, and delivery of scalable data platform features for an enterprise data warehouse and modern data platform. Work across data and AI teams to translate business needs into durable data and AI solutions that support analytics, machine learning, and GenAI use cases.

Key Responsibilities

Lead analysis, design, development, and implementation of scalable data solutions for the enterprise data warehouse and modern data platform.
Collaborate with Product Engineers, Data Engineers, Architects, Data Scientists, and AI Engineers to convert business requirements into scalable data and AI solutions.
Design, build, and optimize ETL/ELT pipelines using Snowflake, Nexla, SQL, and Python.
Develop and manage Apache Airflow DAGs for reliable, observable scheduling and data processing.
Design AI-ready data architectures for analytics, machine learning, GenAI, RAG, vector databases, and agentic AI solutions.
Use AI-assisted development tools (Microsoft Copilot, Claude, Cursor) to speed delivery and improve code quality and documentation.
Define data ingestion, transformation, governance, and serving patterns for AI model training and inference workloads.
Support metadata management, data lineage, data observability, and governance practices for enterprise AI initiatives.
Assess and recommend emerging AI technologies, frameworks, and best practices to strengthen engineering capability.
Manage agile delivery priorities including sprint execution, backlog refinement, story estimation, dependency tracking, and delivery commitments.
Coordinate with onsite, offsite, offshore, and cross-functional teams to remove blockers and maintain delivery momentum.
Create and maintain technical design documentation including design docs, data flow diagrams, AI solution architecture diagrams, and operational runbooks.
Drive data quality, performance tuning, monitoring, and production support for critical pipelines and Snowflake workloads.
Lead architecture reviews, code reviews, root-cause analysis, and problem-solving sessions with engineering and business partners.
Mentor team members on modern data engineering practices and AI-assisted development approaches.
Promote responsible AI practices focused on data privacy, security, governance, and ethical AI use.

Required Qualifications

Bachelor’s degree in computer science, IT, engineering, data science, AI, or a related field, or equivalent experience.
Strong hands-on Snowflake experience, including SQL development, performance tuning, data modeling, stored procedures, and scalable processing patterns.
Hands-on ETL/ELT experience with Nexla and related modern data integration tools.
Experience with Apache Airflow (or similar), including DAG development, scheduling, monitoring, and troubleshooting.
Strong proficiency in SQL and Python for enterprise-scale data engineering.
Practical experience with AI-powered development tools such as Microsoft Copilot, Claude, Cursor, GitHub Copilot, or similar.
Working knowledge of GenAI concepts including LLMs, RAG, vector embeddings, prompt engineering, and agentic AI patterns.
Experience designing data architectures and pipelines that support AI, ML, analytics, and intelligent automation.
Knowledge of data governance, security, privacy, lineage, and compliance for AI and enterprise data platforms.
Proven ability to lead sprint execution, manage competing priorities, clarify scope, identify risks, and coordinate delivery.
Ability to collaborate across product engineering, data engineering, AI/ML, BI/analytics, QA, architecture, and business teams.
Experience working in an Agile environment with onsite, offsite, offshore, and distributed teams.
Strong analytical, problem-solving, communication, and troubleshooting skills.
Self-starter with ownership, accountability, and attention to detail in a fast-paced environment.
Demonstrated ability to learn and adopt emerging AI technologies in a rapidly evolving landscape.

Technologies

Snowflake
Nexla
SQL
Python
Apache Airflow
Microsoft Copilot
Claude
Cursor
GitHub Copilot
LLMs
Retrieval-Augmented Generation (RAG)
Vector databases
Apache Airflow DAGs
Vector embeddings

Preferred Qualifications

Experience with Snowflake on Azure and related Azure data services.
Experience with Azure OpenAI, Microsoft Fabric, Databricks, Amazon Bedrock, Anthropic Claude, OpenAI, or similar AI platforms.
Experience implementing RAG, semantic search, vector databases, or agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
Experience building AI-enabled data products, intelligent document processing, conversational AI, or enterprise knowledge retrieval systems.
Experience with additional ETL/ELT tools including Azure Data Factory, Talend, and Python-based frameworks.
Experience with DevOps, CI/CD, Infrastructure-as-Code, and MLOps supporting AI/data platform deployments.
Experience with Jira, Azure DevOps, GitHub, or similar Agile delivery and tracking tools.
Experience with metadata management, data quality frameworks, or Master Data Management (MDM) initiatives.
Experience in insurance, insurtech, healthcare, or financial services.
Relevant certifications in Snowflake, Azure, Generative AI, Claude, Microsoft Copilot, AI engineering, or cloud data platforms.

Compensation and Location

Salary: USD 103,000 - 126,000 per year
Work location: In person
City: Dallas, TX
Work setting: Onsite

Lead Data Engineer