Data Engineer II - Edison, NJ - ST Jobs in Edison, NJ at Top Level Recruiter

Role Overview:
As a Data Engineer, you will be responsible for designing, developing, and maintaining scalable data pipelines and solutions that support data integration, transformation, and analytics. You will work closely with data scientists, analysts, and business stakeholders to ensure high-quality, reliable data is available for decision-making. This role requires hands-on technical expertise along with problem-solving and collaboration skills.

Key Responsibilities:

• Design, build, and optimize data pipelines and ETL/ELT processes for structured and unstructured data.

• Develop and maintain data models, schemas, and storage solutions across cloud/on-premises platforms.

• Ensure data quality, governance, and compliance with security standards.

• Collaborate with analysts, engineers, and product teams to understand business requirements and deliver reliable datasets.

• Monitor and troubleshoot data pipelines, ensuring high availability and performance.

• Support automation and continuous integration/deployment of data workflows.

• Contribute to best practices for data engineering and mentor junior engineers when needed.

Required Skills & Experience:

• Proven experience as a Data Engineer or in a similar data-focused role.

• Strong proficiency in SQL and relational databases (e.g., PostgreSQL, MySQL, SQL Server, Oracle).

• Hands-on experience with big data tools (Hadoop, Spark, Kafka) or modern cloud data platforms (AWS Redshift, Google BigQuery, Azure Synapse, Snowflake).

• Proficiency in at least one programming language (Python, Java, or Scala).

• Experience with ETL/ELT tools (Informatica, Talend, dbt, Airflow, etc.).

• Familiarity with data warehousing, data lakes, and modern data architecture.

• Understanding of APIs, microservices, and integration patterns.

• Strong problem-solving skills and ability to work in cross-functional teams.

Preferred Qualifications:

• Knowledge of containerization and orchestration (Docker, Kubernetes).

• Experience with CI/CD pipelines and DevOps practices.

• Exposure to machine learning pipelines or analytics platforms.

• Strong documentation and communication skills.