Data Engineer

SZNS Solutions

Data Engineer turning raw data into actionable intelligence for cloud solutions. Build data pipelines for AI workflows in a technology consulting company.

Posted 4/17/2026full-timeReston • Virginia • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowApacheBigQueryCloudGoogle Cloud PlatformJavaKafkaMongoDBNeo4jNoSQLPostgresPythonRustSparkSQL

About the role

Key responsibilities & impact

Architect and deploy pipelines to ingest, transform, and store data from high volume, disparate sources for real-time analysis
Create a highly reliable single source of truth for enterprise intelligence and enablement
Architect and optimize production-grade data foundations to support high-performance AI workflows and automated decision-making.
Establish and automate strict data security, quality assurance, and governance processes. Design systems for high fault tolerance and rapid disaster recovery
Design and model for efficient queries, resource usage, workload scheduling, and cost

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
5+ years of experience in data engineering, within a cloud environment, demonstrating a clear progression from engineering into architectural design
Proficiency in SQL and strong programming skills in Python, Rust, or Java
Experience building and maintaining data pipelines using processing/streaming frameworks (e.g., Kafka, Flink, Beam, Spark) and orchestration tools (e.g., Airflow)
Experience architecting data stores and schemas for AI workflows (e.g., RAG)
Active Google Cloud certifications, or willingness to obtain within one month of joining
Builder mentality and bias for action
US Citizen
Preferred Qualifications
Deep expertise in the Google Cloud Platform (GCP) ecosystem, specifically building streaming and batch pipelines using Dataflow (Apache Beam), Pub/Sub, BigQuery, and Cloud Composer (Airflow)
Strong background in data modeling and architecture across relational (e.g., PostgreSQL), NoSQL (e.g., Firestore, MongoDB), and graph databases (e.g., Neo4j), including modern cloud data warehouses (e.g., BigQuery) and data lakes (e.g., GCS, Dataproc)
Demonstrated experience setting up infrastructure for modern data science, machine learning, or Generative AI (e.g., preparing unstructured data, vector databases, RAG pipelines)
Familiarity with regulatory compliance frameworks (FedRAMP, HIPAA, etc.) and security strategies
Experience with modern data platforms like Snowflake or Databricks

Benefits

Comp & perks

Competitive salary and benefits package
Hybrid work environment (MWF in-person in our Reston office)
A collaborative and innovative work environment
Continuous learning and development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

SQLPythonRustJavadata engineeringdata pipelinesdata modelingdata architecturemachine learningGenerative AI

Soft Skills

builder mentalitybias for action

Certifications

Google Cloud certifications