Hybrid Data Engineer presso Weekday

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.