Data Engineer

Role Summary We are seeking a highly skilled and motivated Data Engineer to join our team. This role is central to designing, building, and maintaining the systems and infrastructure for data storage, processing, and analysis. You will work within a multidisciplinary Agile team to deliver high-quality data products, support complex AI/ML initiatives, and advance the company's data-driven decision-making capabilities. We are looking for hands-on expertise in cloud data services, with a strong focus on a major public cloud platform, preferably Google Cloud Platform (GCP) . Key Note & Application Instructions To apply, send your current CV directly to . Please note: Due to high application volumes, only candidates who meet the outlined requirements will be contacted for further discussion. To be considered, you MUST have the following minimum experience: Data Engineering Experience: 3+ years of experience as a Data Engineer. Core Programming: Strong proficiency in Python and expert knowledge of SQL . Cloud Platform: Proven, hands-on experience with a major cloud platform, ideally GCP (BigQuery, Dataflow, etc.) or similar cloud data services. Key Responsibilities The successful candidate will be responsible for a range of tasks, from core data pipeline development to supporting cutting-edge machine learning projects: Data Pipeline Development: Lead the design, development, and optimization of distributed, reliable, and scalable data pipelines, ensuring adherence to modern ELT/ETL principles and business goals. Cloud Platform Execution: Utilize public cloud data services (e.g., BigQuery, Dataflow, Dataproc, Cloud Spanner) to build, implement, and operate critical data solutions. Architecture & Ingestion: Design and implement versatile data ingestion patterns that support batch, streaming, and API interfaces for data ingress and egress. Code & Standards: Develop custom code and frameworks using best practices in Python and advanced SQL . Provide technical guidance and ensure high standards for code quality and operational efficiency. AI/ML Support: Engineer effective features for modeling, collaborate with ML Engineers and Data Scientists, and support the deployment and monitoring of AI/ML models, including those related to Generative AI initiatives. Operational Excellence: Implement application logging, job monitoring, and performance monitoring. Clean, prepare, and optimize data for further analysis and modeling. Collaboration: Work closely with cross-functional Agile teams, solutions architects, and business stakeholders, providing technical expertise and mentoring junior data professionals on data standards and practices. Required Skills & Qualifications Technical Skills (Must-Haves) Experience: 3+ years of experience as a Data Engineer, preferably in a large organization. Programming: Strong proficiency in Python (PySpark, pandas) and expert knowledge of SQL . Cloud Platform: Proven, hands-on experience with a major public cloud platform, especially Google Cloud Platform (GCP) , including services like BigQuery, Dataflow, and Dataproc . Big Data: Experience with large-scale data processing technologies like Spark . Data Warehousing: Experience with designing and maintaining data warehouses and/or data lakes. DevOps/Tooling: Familiarity with software development tooling ( Git, CI/CD, JIRA ) and software engineering fundamentals. Methodology: Strong understanding of Agile methodologies with a focus on iterative delivery. Nice-to-Have Skills (A Competitive Edge) Experience with DBT (data build tool) for transformation orchestration. Exposure to Gen AI models, LLMs (Large Language Models), Vector DB/Embeddings , or Agentic AI frameworks. Familiarity with containerization ( Docker, Kubernetes ) and MLOps principles/tools (MLFlow, Kubeflow). Knowledge of distributed databases (e.g., Redshift, Snowflake) or Big Data concepts like Hadoop. Experience with other cloud platforms (Azure, AWS). Experience with Flask/Django for developing APIs. Mindset & Behaviors Delivery Focus: "Fail-fast, succeed early" mindset with a focus on measurable outcomes and quality assurance. Communication: Excellent communication, collaboration, and listening skills to effectively partner with multidisciplinary technical and business teams. Adaptability: Flexibility to changes in work direction and a strong ability to solve complex data problems at an abstract level.