Data Engineer

SZNS; Reston (VA); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Competitive salary and benefits package

Hybrid work environment

Collaborative work environment

Continuous learning and development opportunities

Job summary

SZNS, based in Reston, VA, is seeking a Data Engineer to transform raw data into actionable intelligence. The ideal candidate has 5+ years of experience in data engineering, strong proficiency in SQL and programming skills in Python, Rust, or Java, alongside experience with cloud-based data pipelines and architectures. This role offers a competitive salary, benefits package, and a hybrid work environment, emphasizing continuous learning and innovation.

Qualifications

• 5+ years of experience in data engineering, within a cloud environment.
• Experience building and maintaining data pipelines using processing/streaming frameworks.
• Active Google Cloud certifications or willingness to obtain.

Responsibilities

• Architect and deploy ETL/ELT pipelines for data processing.
• Create a reliable single source of truth for enterprise intelligence.
• Establish data security and governance processes.

Skills

SQL

Python

Rust

Java

Kafka

Flink

Beam

Spark

Airflow

Education

Bachelor’s degree in Computer Science, Engineering, or equivalent

Tools

Google Cloud Platform

BigQuery

Dataflow

Pub/Sub

Cloud Composer

PostgreSQL

MongoDB

Neo4j

Snowflake

Databricks

Job description

"SZNS Solutions (pronounced "seasons") is a technology consulting company and Google Cloud Partner based in Reston VA. We specialize in delivering agentic AI and cloud computing solutions. Founded by ex-Googlers with engineers from Google, Amazon, and Capital One, SZNS differentiates itself particularly in AI, data engineering, blockchain, and cloud-native software application development."

Role Summary

The Data Engineer is the bedrock of intelligence operations, responsible for turning raw, unstructured data into actionable intelligence. You will embed directly with clients to build the data pipelines that power AI workflows. We build systems that don't just move data, but do it with the speed and reliability required for live and automated intelligence. Ultimately, you’ll create the foundations that will influence key strategic decisions for us and our customers.

Key Responsibilities

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

Minimum Qualifications

• 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

• 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