Senior Data Engineer

Careers at Lineate•Yerevan, GA

14d

About The Position

Lineate is a US-based international software development company with over two decades of experience. From Intelligent Document Processing(IDP) and Agentic RAG systems to scalable cloud architectures, we turn complex ideas into real, measurable results. We deliver AI-driven custom solutions for FinTech, HealthTech, AdTech, and beyond, empowering businesses to grow smarter, faster, and more efficiently. Our expertise falls into three main categories: Building Custom AI Solutions: Deploying high-impact, AI-enabled technology utilizing IDP, Agentic RAG. Cloud and Data Infrastructure: Optimizing business operations with our data management and cloud computing solutions. Team Augmentation: Providing specialized experts in FinTech, AdTech, and HealthTech to integrate seamlessly and accelerate project timelines. Our goal is not just to build technology, but to build the future operating model for our clients. Product: A US-based real estate and mortgage technology platform that supports the home-buying journey from property search through closing. It combines property search, data integrations, workflow automation, real estate services, and home-buying rewards in a single customer journey.

Requirements

6+ years of hands-on Data Engineering experience.
Strong Python and SQL skills.
Solid production experience with ETL/ELT, data integration, and data modeling.
Hands-on experience cleaning and mapping incomplete, inconsistent, or duplicated data from multiple sources.
Experience matching records without a reliable common ID, using composite keys, rule-based or fuzzy matching, entity resolution, or similar approaches.
Production experience with modern data technologies such as Airflow, dbt, Spark, Kafka, ClickHouse, Snowflake, Databricks, or similar.
Strong understanding of data quality and DWH/data platform principles.

Responsibilities

Build and maintain reliable, scalable ETL/ELT pipelines and data platform components.
Integrate data from multiple heterogeneous sources, including databases, APIs, files, and third-party systems.
Clean, normalize, transform, and map messy, incomplete, and inconsistent data, often without reliable unique keys.
Develop solutions for record matching, deduplication, reconciliation, and entity resolution across different data sources.
Design data models and implement data quality, validation, and monitoring processes.
Investigate data discrepancies, troubleshoot pipelines, and continuously improve data reliability and performance.