Freelance Data Engineer (Security)

Freelance Data Engineer (Security)

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 Lambda

• 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.