Data Engineer

DeepLightAI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, withparticular depthin financial services and banking. Our team combines deepexpertisein data science, statisticalmodeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations. The Data Engineeris responsible fordesigning, implementing, and optimising data pipelines and infrastructure to support ourcutting-edgeAI systems. The Data Engineer collaborates closely with our multidisciplinary team to ensure the efficient collection, storage, processing, and analysis of large-scale data, enabling us to unlock valuable insights and drive innovation across various domains. Responsibilities Design, build, andoptimisescalable data solutions, primarilyutilisingthe Lakehouse architecture to unify data warehousing and data lake capabilities. Advise stakeholders on the strategic choice between Data Warehouse, Data Lake, and Lakehouse architectures based on specific business needs, cost, and latency requirements. Design, develop, andmaintainscalable and reliable datapipelines to ingest, transform, and load diverse datasets from various sources, including structured and unstructured data, streaming data, and real-time feeds. Implement standards and tooling to ensure ACID properties, schema evolution, and high data quality within the Lakehouse environment. Implement robust data governance frameworks (security, privacy, integrity, compliance, auditing). Continuouslyoptimizedata storage, compute resources, and query performance across the data platform to reduce costs and improve latency for both BI and ML workloads,leveragingtechniques such as indexing, partitioning, and parallel processing. Develop andmaintainCI/CDpipelines to automate the entire machine learning lifecycle, from data validation and model training to deployment and infrastructure provisioning. Deploy, manage, and scale machine learning models into production environments, utilizingMLOpsprinciples for reliable andrepeatable operations. Establishand manage monitoring systems to track model performance metrics, detect data drift (changes in input data), and model decay (degradation in prediction accuracy). Ensure rigorous version control and tracking for all components: code, datasets, and trained model artifacts (using tools likeMLflowor similar). Create comprehensive documentation, including technical specifications, data flow diagrams, and operational procedures, tofacilitateunderstanding, collaboration, and knowledge sharing. Benefits & Growth Opportunities Competitive salary and performance bonuses Comprehensive health insurance Professional development and certification support Opportunity to work on cutting-edge AI projects Flexible working arrangements Career advancement opportunities in a rapidly growing AI company This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients. Qualifications Proven practical experience in designing, building, andoptimisingsolutions using Data Lakehouse architectures (e.g., Databricks, Delta Lake). Strong hands‑on experience with managing data ingestion, schema enforcement, ACID properties, andutilizingbig data technologies/frameworks like Spark and Kafka. Expertisein datamodeling,ETL/ELTprocesses, and data warehousing concepts. ProficiencyinSQLand scripting languages (e.g.,Python, Scala). Demonstratedpractical experienceimplementingMLOpspipelinesfor production systems. This includes a solid understanding and implementation experience withMLOpsprinciples: automation, governance, and monitoring of ML models throughout the entire lifecycle. Experience withCI/CDtools, containerization/orchestration technologies (e.g.,Docker, Kubernetes), model serving frameworks (e.g., TensorFlow Serving,Sagemaker), and experiment tracking (e.g.,MLflow). Experience with production monitoring tools to detectdata driftormodeldecay. Strong hands‑on experience with majorcloud platforms(e.g., AWS, Azure, GCP) and familiarity withDevOpspractices. Excellent analytical,problem-solving,and communication skills, with the ability to translate complex technical concepts into clear and actionable insights. Proven ability to work effectively in a fast-paced, collaborative environment, with a passion for innovation and continuous learning #J-18808-Ljbffr