Own the reliability, scalability, and performance of core data pipelines and infrastructure.
Lead complex data engineering initiatives from architecture and planning through implementation.
Set engineering standards across Python, SQL, orchestration, testing, documentation, governance, and monitoring.
Conduct design and code reviews, share technical knowledge, and guide engineers as the team grows.
Partner with Data Science, BI, and business teams to deliver data solutions for analytics and machine learning.
🛠️ Requirements
At least 5 years of hands-on experience in data engineering, particularly in designing, building, and maintaining production data pipelines and cloud-based data infrastructure.
Strong hands-on experience with dbt (Data Build Tool) for data transformation, modelling, and management of analytics workflows.
Solid experience with Google Cloud Platform (GCP), particularly BigQuery and other relevant data processing and storage services.
Proven experience using Apache Airflow for workflow orchestration, scheduling, monitoring, and managing data pipelines.
Strong proficiency in Python and SQL, with the ability to develop scalable data solutions and troubleshoot complex technical issues.
Good understanding of data architecture, data warehousing, ETL/ELT processes, performance optimisation, data quality, monitoring, and governance.
Experience designing technical solutions, leading complex data engineering projects, and taking ownership of delivery and ongoing system performance.
Ability to establish engineering standards, conduct code reviews, provide technical guidance, and share knowledge with other engineers.
Familiarity with AI-assisted development, automation, or modern engineering practices to improve development productivity, data quality, and operational efficiency.
Strong problem-solving and decision-making skills, with the ability to balance technical priorities and business requirements and communicate effectively with technical and nontechnical
Full job description
Own the reliability, scalability, and performance of the company's core data pipelines and infrastructure. Set technical priorities, identify risks, and drive improvements to ensure data remains accurate, accessible, and dependable as the business grows.
Lead the design and delivery of complex data engineering initiatives, from architecture and technical planning through implementation. Make sound technical decisions, balance competing priorities, and ensure solutions address immediate business needs while remaining scalable and maintainable.
Establish and reinforce engineering standards across Python, SQL, orchestration, testing, documentation, data governance, and monitoring. Leverage AI and automation to reduce repetitive tasks, such as test and documentation generation and early detection of data quality issues, allowing the team to focus on more complex engineering challenges.
Set the technical direction and quality standards for data engineering. Conduct design and code reviews, share technical knowledge, and support the onboarding, development, and guidance of engineers as the team grows.
Partner with Data Science, BI, and business teams to translate data requirements into practical technical solutions. Anticipate dependencies, resolve complex issues, and strengthen the data foundation supporting analytics, machine learning, and business decision-making.
Requirements
At least 5 years of hands-on experience in data engineering, particularly in designing, building, and maintaining production data pipelines and cloud-based data infrastructure.
Strong hands-on experience with dbt (Data Build Tool) for data transformation, modelling, and management of analytics workflows.
Solid experience with Google Cloud Platform (GCP), particularly BigQuery and other relevant data processing and storage services.
Proven experience using Apache Airflow for workflow orchestration, scheduling, monitoring, and managing data pipelines.
Strong proficiency in Python and SQL, with the ability to develop scalable data solutions and troubleshoot complex technical issues.
Good understanding of data architecture, data warehousing, ETL/ELT processes, performance optimisation, data quality, monitoring, and governance.
Experience designing technical solutions, leading complex data engineering projects, and taking ownership of delivery and ongoing system performance.
Ability to establish engineering standards, conduct code reviews, provide technical guidance, and share knowledge with other engineers.
Familiarity with AI-assisted development, automation, or modern engineering practices to improve development productivity, data quality, and operational efficiency.
Strong problem-solving and decision-making skills, with the ability to balance technical priorities and business requirements and communicate effectively with technical and nontechnical
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Build and improve a scalable data platform and tools that support analytics, machine learning, and AI products, including semantic-layer and LLM-powered data capabilities.
Lead data infrastructure for client reporting, measurement, operations, and AI-driven work by building trusted Snowflake pipelines, shaping technical strategy, and growing a data support team.
Design and scale data pipelines from raw ingestion through curated analytics layers, set technical standards for data infrastructure, and mentor engineers while partnering with analytics, product, and engineering teams.
Leads data engineering work for defense missions, translating stakeholder requirements into scalable Databricks pipelines and data models while guiding engineers and improving pipeline performance, reliability, and integrity.
