Data Engineer - Security

Freelance Data Engineer (Security)

Jack IQ Programme  · 

Role details

Job title | Data Engineer (Security)

Reports to | Product Lead / Technology Executive

Contract type  Freelance (to perm possible)

Duration ASAP – April 27

Location / working pattern  - Remote

Start date ASAP

Purpose of the role

Jack Morton is seeking a Data Engineer to help build out the data spine for an AI-enabled event intelligence platform. The role will focus on creating secure and scalable processes to connect operational systems, unstructured project documentation and data from third-party platforms into existing data warehousing infrastructure.

The successful candidate will help transform fragmented agency knowledge into governed, searchable, AI-ready assets while ensuring adherence to privacy, retention, and client-specific data requirements.

Key responsibilities

• Create a structured data layer to integrate and model structured data from API enabled sources such as CRM systems, Azure databases, event attendance systems, survey platforms, and financial warehouses.
• Create ingestion pipelines for the unstructured knowledge layer that includes Powerpoint presentations, Word documents and unstructured Excel spreadsheets.
• Build an AI-Ready document infrastructure that can classify documents, handle versioning, track provenance, and manage metadata, auditability and permissioning.
• Implement frameworks for strictest confidentiality and privacy protections, including access controls and security roles, consent tracking, data retention and data deletion management.
• Work with members of the Jack Morton team to clearly communicate limitations, timeline expectations, out-of-pocket cost expectations and repercussions to platform design.
• Participate in planning discussions on platform roadmap with Jack Morton team members.

Data engineering requirements

Pipeline design, ingestion, schema, data quality, orchestration, tooling.

This role requires strong experience in the following:

• Python
• SQL
• Data Modeling
• Data Warehousing (Snowflake experience is desirable)
• ETL or ELT development
• API Integrations
• Batch and event-driven pipelines.

The applicant should understand:

• Metadata extraction
• OCR workflows
• Semantic indexing
• Vector databases
• Retrieval-Augmented Generation (RAG)
• Chunking strategies
• Document classification
• Embedding pipelines

Security requirements

Access control, tenant isolation, key management, pseudonymisation, audit and logging, compliance frameworks.

Experience with the following is essential, with AI-specific experience being a bonus:

• PII handling and understanding of GDPR implications
• Data pseudonymization
• Data lineage
• Retention policies
• Audit logging
• Data access controls

Platform and tooling

Snowflake, AWS, Bedrock, integration patterns, languages, infrastructure as code.

• Snowflake
• AWS Cognito
• AWS S3
• AWS Lamda
• AWS DynamoDB

Experience required

• Azure or Snowflake data warehousing
• Document ingestion and RAG experience
• Data governance and security
• Modern data modeling skills
• API integration
• Data pipelines for unstructured documentation
• Experience explaining technical infrastructure to non-technical teams

Experience preferred

• LLM model building
• Understanding of event and experiential marketing
• Experience working with a project-based business

First 90 days

What success looks like by day 30, 60 and 90.

Within the first 90 days the data engineer should be integrated into the platform team. They should have delivered a proposal for their plan for the data spine and should be beginning to build out the initial processes to bring it to life.