Senior Data Platform Architect – AI & Lakehouse Leader

Sigmoid; New Jersey; On-site; USD 180,000 - 240,000; Full time

Job summary

Sigmoid is seeking an experienced Solutions Architect to define and lead the cloud-native data platform strategy across ingestion, transformation, storage, governance, and secure access. You will build and optimize lakehouse capabilities on Snowflake and/or Databricks, ensuring high-performance, scalable data solutions for enterprise clients.

The role requires deep expertise in Spark, Iceberg, and modern data architectures, with strong collaboration across Product, Analytics, and ML teams.

Qualifications

• 15+ years in software and/or data engineering with progression to technical leadership.
• Proficiency writing production-quality Python/SQL code and strong testing practices.
• Experience with microservices, REST/gRPC, and event-driven systems like Kafka.
• Hands-on Spark, Snowflake/Databricks, and cloud-native data pipelines.
• Knowledge of data governance, cataloging, and lakehouse architectures.

Responsibilities

• Define platform vision and roadmap for a cloud-native data platform.
• Build lakehouse architecture and data pipelines on Snowflake/Databricks.
• Optimize performance of Iceberg tables and multi-engine queries.
• Establish CI/CD, IaC, testing, and data quality standards.

Skills

Python

SQL

Java/Scala

Microservices

REST/gRPC APIs

Kafka

Java (Spring Boot)

Apache Spark

Snowflake/Databricks

Airflow/dbt/Databricks Workflows

AWS

GCP/Azure

Data modeling

ETL/ELT

Iceberg

Unity Catalog / Glue Catalog

S3/Cloud data lake

Databricks Trino/Presto

AI/ML & LLM-based analytics

Education

BS in Computer Science/Engineering

MS in Computer Science/Engineering

Tools

Git

GitHub/GitLab

Jira

Confluence

Slack

Job description

Sigmoid is seeking an experienced Solutions Architect to define and lead the cloud-native data platform strategy across ingestion, transformation, storage, governance, and secure access. You will build and optimize lakehouse capabilities on Snowflake and/or Databricks, ensuring high-performance, scalable data solutions for enterprise clients.

The role requires deep expertise in Spark, Iceberg, and modern data architectures, with strong collaboration across Product, Analytics, and ML teams.