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# Data Engineer IV

- **Company:** Planet DDS
- **Location:** Glasgow, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, BigQuery, Code Review, Information Engineering, Data Governance, Data Infrastructure, Data Security, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Construction, SQL Databases, Data Streaming, Data Logging, Cloud Platform System, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Database Performance, Data Strategy, Data Lakes, Information Technology, Data Analytics, Apache Kafka, Data Management, Machine Learning Operations, Data Delivery, Software Version Control, Data Pipelines, Amazon Redshift, Databricks, Programming Languages
- **Published:** September 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e10ff70cafcab797

## About the Role

* 8-11+ years of experience in data engineering, with demonstrated progression to senior or staff-level responsibilities.
 * Expert-level proficiency in SQL and at least one programming language (Python, Scala, or Java).
 * Deep experience designing and building data pipelines using tools such as Apache Spark, Airflow, dbt, or equivalent.
 * Proven ability to design and implement cloud-based data platforms (AWS, Azure, or GCP), including data lake and warehouse solutions (Databricks, Snowflake, Redshift, BigQuery, or similar) and AI/ML platforms.
 * Strong understanding of data modeling, schema design, and database performance optimization for large-scale datasets.
 * Experience with real-time and streaming data architectures (Kafka, Kinesis, or equivalent).
 * Hands-on experience integrating AI/ML workflows into data platforms, including feature stores, vector databases, or LLM-adjacent data pipelines (e.g., RAG systems, embedding pipelines).
 * Familiarity with AI development tooling and a demonstrated habit of using AI to accelerate engineering work, code review, documentation, and data modeling.
 * Demonstrated ability to lead technical projects and influence architectural decisions across teams and functions.
 * Experience with data governance, data quality frameworks, and compliance requirements; familiarity with HIPAA is strongly preferred.
 * Excellent communication skills with the ability to translate complex technical concepts for non-technical stakeholders.
 * Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.

## Description

* Architect and build AI-ready data pipelines and ELT workflows with automation at the core, enabling real-time analytics, ML model serving, and generative AI applications across product and operations.
 * Lead the design of a modern data platform built for AI workloads, including vector stores, feature stores, and infrastructure that supports LLM integration and AI-powered analytics.
 * Partner with Product, Engineering, and Data Science teams to define data models and ensure high-quality, reliable data delivery across the organization.
 * Define and enforce data and software engineering best practices, including version control, data quality standards, documentation, and testing frameworks.
 * Mentor and coach junior and mid-level data engineers, fostering a culture of technical excellence and continuous growth.
 * Evaluate, adopt, and champion new data technologies and frameworks to drive platform modernization and efficiency.
 * Collaborate with senior leadership to define the long-term data strategy and roadmap aligned with company objectives.
 * Lead complex, cross-functional data initiatives from scoping through delivery, ensuring stakeholder alignment and on-time execution.
 * Optimize data infrastructure for performance, cost efficiency, and reliability at scale.
 * Ensure data security, compliance, and governance across the data platform, including adherence to HIPAA and other applicable regulations.
 * Champion AI tooling adoption across the data team, including AI-assisted development, automated data quality monitoring, anomaly detection, and intelligent pipeline observability.
 * Design and maintain data products that serve AI/ML models in production, including feature engineering pipelines, model input/output logging, and feedback loop infrastructure.