Engineering Manager - Data Platforms

At Licious, we believe great customer experiences start with reliable operations. Behind every perfectly chilled product and seamlessly running Distribution Centre is a maintenance team that keeps our business moving. If you're someone who enjoys solving technical challenges, driving operational excellence, and ensuring critical assets perform at their best, this could be the role for you.

Think you have what it takes to be the magic ingredient in the recipe that is Licious? Read on...

What makes the role meaty?

• Architect the Data Platform: Architect, build, and maintain scalable and secure data infrastructure using tools like Apache Hadoop, Hive, Spark, Kafka, Airflow, and Delta Lake.
• Data Pipelines & Modeling: Develop robust ETL/ELT pipelines, data models, and streaming data workflows to support analytics, business intelligence, and machine learning use cases.
• Cloud & Modern Data Stack: Optimize data storage and compute using cloud-native solutions (AWS S3, Redshift, EMR, Glue, Athena) and integrate with modern data stack tools such as dbt, Snowflake, BigQuery, and Fivetran.
• Data Quality & Governance: Ensure data quality, lineage, cataloging, and observability using tools like Apache Atlas, Great Expectations, and Amundsen.
• ML & Advanced Analytics Enablement: Support Data Science and AI/ML teams by maintaining model pipelines and training infrastructure, enabling MLOps frameworks (MLflow, SageMaker, PyTorch, TensorFlow) for seamless experimentation and deployment.
• Team Leadership: Hire, coach, and mentor a team of 8–10 data engineers; set clear objectives, track performance, and build a culture of ownership and continuous learning.
• Cross-functional Partnership: Collaborate closely with Product, Engineering, and Data Science teams to deliver accurate, timely, and actionable data, translating business needs into scalable engineering solutions.

We hope that you have...

• Experience: 8+ years of experience in data engineering or related infrastructure roles, with at least 3 years in a technical leadership or managerial capacity.
• Technical Skills: Strong experience with cloud platforms (AWS preferred), distributed data systems, and large-scale batch + real-time data processing. Hands-on proficiency with tools like Kafka, Airflow, Hadoop, Hive, Spark, dbt, PyTorch, and MLflow.
• Platform Ownership: Proven experience building and maintaining enterprise data platforms and MLOps pipelines, with a strong understanding of data architecture trade-offs and cost optimization.
• Critical Thinking: Exceptional problem-solving skills and the ability to reason about large-scale data systems, architectural trade-offs, and platform scalability.
• Work Style: Strong stakeholder communication and team leadership skills; comfortable owning ambiguous, complex problems while mentoring and growing a team.
• Bias for Action: Demonstrated ability to deliver measurable outcomes — platform reliability, data quality, ML enablement — with genuine curiosity for learning new technologies.

Good to Have: Experience with data privacy regulations (GDPR, SOC2, etc.); exposure to Data Mesh or Lakehouse architecture; experience with AI-enabled platforms, RAG systems, or LLM-based infrastructure.