Experience and Responsibilities:
Bachelor's or master's in Data science, Data Engineering, Computer Science or a related field.
A minimum of 2 years for DE and 4 years SDE experience specifically in a data engineering role, including working with large-scale data platforms.
Design, develop, and maintain robust data pipelines to support analytics and business needs by ingesting and processing data from various sources into the cloud platform.
Collaborate with cross-functional teams, including Data Science and Data Product teams, to define data models, architecture, and transformations using tools like DBT (SQL/Jinja).
Proficient in Python-based data engineering (Mage) and SQL (MySQL, PostgreSQL, NoSQL).
Experience with big data tools like Databricks and Spark and cloud-based platforms like BigQuery for large-scale data analytics.
Skilled in data analysis and able to interpret complex data sets and derive actionable insights.
Define and standardize data assets to ensure high-quality data delivery while supporting the design and development of scalable data architectures and integration solutions.
Ensure data quality, reliability, and security by supporting Data Governance processes.
Able to understand cloud-based infrastructure and have experience working with data visualization tools such as Tableau, Looker Studio, and Power BI.
Knowledge of ETL/ELT best practices, data warehousing concepts, and experience with data modeling techniques.
A basic understanding of Google Tag Manager and Google Analytics setup, testing, and validation is a plus.
Assist the team leader in project management and provide business-as-usual (BAU) support services.
Qualification and Personal Characteristics:
Strong problem-solving and analytical skills with attention to detail.
Excellent communication and teamwork skills, with the ability to work collaboratively with user departments and vendors.
Ability to learn new technologies quickly and innovate within the data space.
Strong customer service mindset, with the ability to train and mentor team members, identifying skills and gaps.
Self-initiative, proactive, and forward-thinking, focusing on continuously improving data platforms and processes.