Senior Data Engineer

Location:

[Location(s) / Remote] – Hybrid – Manhattan, NYC

Employment Type:

Contract

Trianz is an applied AI solutions company that accelerates customer business transformation through AI powered "Transformation Services as a Software Model". With 25+ years of transforming enterprises, we've evolved to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and Sec Ops - delivered through strategic partnerships with leading hyperscalers.

We're building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence - RevolutionAIzing Transformations.

We are seeking a Senior Data Engineer who will design, build, and support data pipelines and analytics datasets that power enterprise reporting across Finance, Technology, and Operations. This role focuses on ingesting data from corporate systems, organizing it in a cloud-based data lake, and enabling reliable reporting using Amazon Quick Sight.

The ideal candidate is a hands-on data engineer who is comfortable working with modern AWS data services, partnering with business stakeholders, and supporting production reporting workloads. This role operates in a growing analytics environment and will help establish practical standards for data ingestion, modeling, and reporting.

Context:
This engineer will build and maintain Python-based ETL pipelines within AWS Glue. They will own transformation logic, manage orchestration and metadata cataloging, and ensure data is securely processed and accessible for downstream analytics and AI tools.

The role requires strong ownership of pipeline reliability, data quality, and performance tuning.

Experience with dbt and Snowflake will become increasingly important as the platform evolves.

• Design, build, and maintain scalable data ingestion frameworks using AWS native services (e.g., Glue, Lambda, S3, Step Functions) and Snap Logic to data into the data lake.
• Architect and manage the enterprise data lake on Amazon S3 using Apache Iceberg, including partitioning strategies, schema evolution, metadata optimization, and lifecycle management.
• Develop robust data transformation pipelines (ELT/ETL) to standardize, cleanse, and enrich source system data for downstream analytics and operational use cases.
• Develop and maintain reporting-ready views and queries using Amazon Athena and AWS Glue metadata.
• Implement and monitor data quality frameworks, including validation rules, reconciliation checks, and anomaly detection to ensure integrity and reliability of enterprise data asset
• Collaborate with Finance, Technology, and Operations stakeholders to translate business requirements into scalable data architecture and transformation logic.
• Establish data governance best practices, including documentation, lineage racking, access controls, and change management processes.
• Monitor pipeline health and data quality, investigate data issues, and support recurring reporting cycles

• Work with Architects, Development teams, Information Security, Infrastructure and Operations to develop requirements for common services and work with the team to create and maintain detailed functional and technical specifications and user guides.
• Ensure Common Services acts like a product with regular updates and a focus on the customer.
• Support the operation…