Big Data Engineer

Cynet Systems•Reston, VA

About The Position

We are seeking a skilled Big Data Engineer to design and implement robust big data architectures. This role involves managing the entire data lifecycle, from ingestion and processing to modeling, and ensuring data reliability and security. You will collaborate with various teams to deliver actionable insights and integrate big data solutions into the organization's digital ecosystem. The ideal candidate will be proficient in cloud platforms like AWS and Azure, along with big data tools such as Hadoop, Spark, and Kafka, and possess strong programming skills in Python and SQL.

Requirements

Bachelor's degree in Computer Science, Data Engineering, or related field.
3–5+ years of experience in big data engineering roles.
Proficiency with big data cloud platforms (AWS, Azure) and tools such as Hadoop, Spark, Kafka.
Strong skills in Python, SQL, and data modeling techniques.
Experience with ETL/ELT processes.
Knowledge of Big Data Architecture.

Nice To Haves

Experience with containerization (Docker/Kubernetes) and CI/CD pipelines.
Familiarity with data governance frameworks and enterprise data architecture.
Knowledge of engineering and construction workflows is a plus.

Responsibilities

Design and implement big data architectures, including ingestion, processing, and modeling of heterogeneous data sources.
Develop and maintain ETL/ELT pipelines for structured and unstructured data.
Work closely with data scientists, analysts, and business SMEs to deliver actionable insights.
Integrate big data solutions with the organization's digital ecosystem and project execution platforms.
Ensure data reliability, security, and compliance with governance policies.
Optimize data workflows for scalability and efficiency.
Explore emerging technologies and frameworks to enhance data engineering capabilities.
Support automation and advanced analytics initiatives across projects.
Provide technical guidance to junior engineers or analysts as needed.
Interact frequently with data scientists, project managers, IT teams, and business stakeholders.