Data Engineering Tech Lead

Siri InfoSolutions Inc; Jersey City (NJ); On-site; USD 140,000 - 210,000; Full time

Job summary

Siri InfoSolutions Inc in Jersey City, NJ, seeks a senior data platform architect/engineer to design, implement, and govern cloud-native data platforms across AWS, Snowflake, and related services for enterprise analytics.

You will lead modernization efforts, guide data modeling, ETL/ELT architectures, and apply GenAI concepts within the SDLC, collaborating with business and technology teams to deliver scalable data solutions for insurance and analytic workloads.

Qualifications

• Extensive experience designing and governing cloud-native data platforms (AWS, Snowflake).
• Expertise in Data Warehousing, data modeling, ETL/ELT architectures.
• Hands-on with Python, PySpark, Informatica, DataStage, and AWS Glue.
• Leadership in modern data platforms, data lakes/lakehouses, and real-time processing.
• Familiarity with GenAI in SDLC and insurance data ecosystems.

Responsibilities

• Design, architect, and implement scalable, secure, and high-performance data platforms for enterprise analytics.
• Partner with business stakeholders to translate objectives into scalable data architecture and roadmaps.
• Evaluate emerging data technologies; include AWS Glue, PySpark, Snowflake, GenAI agents.
• Lead modernization of legacy data ecosystems and migrate to cloud-native platforms.
• Define data integration strategies across core insurance platforms like Duck Creek and related systems.

Skills

Cloud data platforms

Data modeling

ETL/ELT architecture

GenAI in SDLC

Leadership

Tools

AWS

Snowflake

PySpark

Informatica

DataStage

AWS Glue

Job description

Must Have Technical/Functional Skills

Strong experience designing, implementing, and governing modern cloud-native data platforms using AWS, AWS Glue, Snowflake, and related cloud data services. Experience in large-scale data modernization and migration initiatives is preferred.

Proven expertise in Data Warehousing, Data Modeling, ETL/ELT architecture, data integration, data quality, metadata management, and scalable data pipeline design for enterprise analytics and operational workloads

Hands-on experience developing and optimizing data solutions using Python, PySpark, Informatica, DataStage, AWS Glue, Snowflake, and related cloud-native data engineering technologies. Ability to guide engineering teams on architecture, coding standards, and implementation best practices.

Demonstrated leadership in architecting and delivering solutions on modern data platforms, including Data Lakes, Lakehouses, Cloud Data Warehouses, and real-time data processing ecosystems. Hands-on implementation experience is required.

Strong understanding of Generative AI, Agentic AI, and AI-enabled engineering concepts, including practical application of GenAI across the Software Development Lifecycle (SDLC), data engineering workflows, reverse engineering, migration acceleration, code generation, testing, documentation, and productivity optimization.

Familiarity with Insurance industry platforms and core systems, including Policy Administration, Claims, Billing, Underwriting, Data & Analytics ecosystems, and digital modernization initiatives. Knowledge of platforms such as Guidewire, Duck Creek, Majesco, or equivalent insurance systems is desirable.

Strong consulting, solutioning, and stakeholder management skills with the ability to work closely with data, cloud, architecture and business to define transformation roadmaps and deliver business value through data-driven solutions.

Roles & Responsibilities

• Design, architect, and implement scalable, secure, and high-performance enterprise data platforms that support the evolving needs of the Property & Casualty Insurance business, including Underwriting, Claims, Billing, Actuarial, Finance, and Analytics functions.
• Partner closely with business stakeholders, including Underwriting, Actuarial, Claims, Data Office, and Technology teams, to translate business objectives into scalable data architecture, engineering solutions, and actionable roadmaps.
• Evaluate emerging data technologies; solution evaluations - Relevant technologies in current insurance data modernization efforts include AWS Glue, PySpark, Snowflake, GenAI agents, and modern data platforms.
• Lead modernization of legacy data ecosystems and drive migration to cloud-native platforms, data lakes, lakehouses, and modern data warehouses leveraging AWS and related cloud technologies. Modernization efforts involving AWS Glue, Snowflake, and cloud-native architectures have been identified as key transformation priorities.
• Define enterprise data integration strategies across core insurance platforms such as Duck Creek, and adjacent enterprise systems, while enabling seamless integration with third-party data sources and industry providers.