Role : Data Engineering Lead with DQ

Location : Iselin, NJ (Hybrid)

Job Overview

We are seeking a highly skilled and experienced Data Engineering Lead with DQ/BA to join our dynamic team. The successful candidate will be responsible for leading the design, development, and deployment of our data infrastructure. This includes managing and optimizing data pipelines, maintaining data architecture standards, and ensuring high data quality (DQ) and business analysis (BA). The role requires a strategic thinker with strong leadership skills, who can innovate and drive data engineering projects to support business decisions and growth.

Responsibilities

Lead the design and implementation of data solutions, ensuring they meet business requirements and industry practices.

Manage and ensure the quality of data, including data cleansing, data integration, data validation, and data consistency.

Collaborate with stakeholders to understand their data needs, define requirements, and translate these into data solutions.

Oversee the work of data engineering team, providing guidance, support, and feedback.

Required Skills

Data Quality Management: The candidate must have a strong understanding and experience in managing and ensuring the quality of large datasets. This includes data cleansing, data integration, data validation, and data consistency.

Data Engineering: The candidate must have a strong background in data engineering, including experience in designing, building, and maintaining data processing systems.

Business Analysis: The candidate must have experience in business analysis, including understanding business needs, defining requirements, and translating these into data solutions.

The candidate must have a Bachelor's degree in Computer Science, Information Systems, or a related field; a Master's degree is preferred.

Preferred Skills

Database Testing: Experience in testing databases to ensure functionality, performance, reliability, and security.

Data Visualization: Ability to use data visualization tools and software to represent complex data in a comprehensible way.

Machine Learning: Familiarity with machine learning algorithms and their application in data analysis.

Big Data Technologies: Experience with big data technologies like Hadoop, Spark, etc.

Cloud Computing: Familiarity with cloud platforms like AWS, Google Cloud, or Azure.

SQL/NoSQL Databases: Proficiency in SQL/NoSQL databases and their querying languages.

ETL Tools: Experience with ETL tools for data extraction, transformation, and loading.

Data Modeling: Knowledge of data modeling principles and best practices.

Project Management: Experience in managing data projects, including planning, scheduling, and monitoring tasks.

Communication Skills: Strong verbal and written communication skills to effectively collaborate with team members and stakeholders.