Senior Data Engineer — Hybrid: Big Data & ML Scoring

Seamless; United States; Hybrid; USD 140,000 - 190,000; Full time

Job summary

Seamless is seeking a Senior Data Engineer to join our Data Product Team. You will work hands-on with our data universe to improve coverage, accuracy, and infrastructure scalability of our product data.

This hybrid role blends data engineering with analytics and requires owning architecture decisions end-to-end. You will tackle billions of records using Spark, SQL, and AWS services, collaborating on scoring systems and AI-enabled workflows.

Qualifications

• 5+ years of professional data engineering experience, with end-to-end ownership.
• Able to defend architecture decisions with minimal oversight.
• Experience delivering end-to-end data projects across analysis, planning, implementation and testing.
• Ability to work with cross-team data deliverables and adjust scope accordingly.
• Clear communicator documenting architecture decisions and tradeoffs.
• Deep hands-on Spark/PySpark experience and understanding of Spark execution details.

Responsibilities

• Build and maintain PySpark ETL jobs for large-scale data processing.
• Support ML-based scoring models that assess data quality and validity of contact data.
• Write and optimize complex SQL across large lake tables and pipelines.
• Collaborate on architecture decisions for scoring systems and data flow.
• Write unit and integration tests for data transformations and model inference code.
• Work within a medallion-style lake architecture (bronze/silver/gold).
• Identify opportunities for AI and agentic workflows in data acquisition and identity resolution.

Skills

Spark/PySpark

Complex SQL

AWS data services

ETL orchestration

Data analytics

Data quality

Tools

Spark

PySpark

SQL

AWS Glue

EMR

S3

Step Functions

Lambda

EventBridge

Airflow

Job description

Seamless is seeking a Senior Data Engineer to join our Data Product Team. You will work hands-on with our data universe to improve coverage, accuracy, and infrastructure scalability of our product data.

This hybrid role blends data engineering with analytics and requires owning architecture decisions end-to-end. You will tackle billions of records using Spark, SQL, and AWS services, collaborating on scoring systems and AI-enabled workflows.