This role is for one of the Weekday's clients

Min Experience: 2 years Location: new jersy, New York JobType: full-time

We are seeking a highly motivated and detail-oriented Data Engineer with 2–3 years of experience to join our growing technology team. The ideal candidate will have strong expertise in Python and a solid understanding of modern data engineering practices. This role is ideal for individuals who are passionate about building scalable data pipelines, managing large datasets, and enabling data-driven decision-making across the organization. Requirements Key Responsibilities Design, build, and maintain data pipelines: Develop efficient and reliable ETL/ELT workflows to process, transform, and load data from multiple sources into structured formats. Data modeling and architecture: Support the design and optimization of databases, data warehouses, and other storage solutions to ensure high performance, scalability, and availability. Automation and scripting: Leverage Python to automate data workflows, improve data ingestion, and streamline repetitive tasks. Data integration: Work with APIs, cloud platforms, and third-party tools to integrate diverse data sources into unified data platforms. Data quality and validation: Monitor and ensure data accuracy, completeness, and reliability by implementing validation checks and error-handling mechanisms. Collaboration with stakeholders: Partner with data analysts, data scientists, and business teams to understand data requirements and deliver effective solutions. Performance optimization: Identify bottlenecks in existing data systems and optimize pipelines for speed and efficiency. Documentation and best practices: Maintain clear documentation for workflows, systems, and processes, ensuring team members can easily follow and maintain codebases. Required Skills and Qualifications 2–3 years of professional experience in data engineering or related fields. Strong programming skills in Python , with experience in data manipulation libraries such as Pandas, NumPy, and PySpark . Hands-on experience with SQL for querying, analysis, and database management. Familiarity with ETL/ELT frameworks , data integration tools, and best practices. Exposure to cloud platforms such as AWS, Azure, or Google Cloud for data engineering tasks. Understanding of data warehousing concepts and experience with platforms like Snowflake, Redshift, or BigQuery is a plus. Solid grasp of data structures, algorithms, and optimization techniques for handling large datasets. Knowledge of version control systems (e.g., Git) and collaborative coding practices. Strong problem-solving skills, attention to detail, and ability to troubleshoot complex data issues. Excellent communication skills to interact with both technical and non-technical stakeholders. Preferred Skills (Good to Have) Experience with workflow orchestration tools such as Apache Airflow or Prefect. Familiarity with NoSQL databases like MongoDB, Cassandra, or DynamoDB. Understanding of data governance, data security, and compliance practices . Exposure to containerization (Docker) and CI/CD pipelines for deployment automation. Knowledge of basic machine learning workflows to support data science teams.