Tech Lead Data

Zinkworks partners with leading Telecommunications and Financial Services organizations to modernize legacy systems migrate mission-critical platforms to the cloud and engineer AI-driven automation. From OSS transformation to rApp development and network intelligence our teams simplify complexity and turn it into competitive advantage. Based in Ireland and operating across the EU UK and US Zinkworks combines deep domain expertise with delivery excellence to help clients modernize faster and operate smarter.

Were forming a dedicated delivery squad for a high-growth international technology client. The goal is to move customer support from reactive to proactive. The new platform will detect service issues from the clients operational data assess their business impact trigger remediation and let affected customers know before they have to ask.

Its a greenfield data- and AI-driven build on Google Cloud delivered in phases. It starts with incident detection and human-approved remediation then adds more granular detection and broader automated actions and ends with a closed-loop system that acts autonomously within defined safety limits. Youll join a squad of around nine people including a Solution Architect data ML backend frontend UX QA and platform engineers working in the clients own tooling (Jira Git CI/CD).

Youll be the technical lead for the squad and the owner of its data foundations. Every detection score and remedy in this platform depends on data arriving on time joined correctly and trusted so youll design and build the ingestion modelling and quality layers while guiding the engineering team day to day alongside the Solution Architect.

• Lead the squad technically: set engineering standards guide design decisions review code and unblock the team working with the Solution Architect on design authority and integration contracts.

• Build ingestion for multiple feeds including high-frequency telemetry with historical backfill customer support data business data and third-party performance data.

• Design a canonical data model with cross-source validation.

• Build data quality freshness and completeness monitoring and serve curated marts and feature pipelines for the ML team.

• Scale ingestion to high-volume granular data with geographic enrichment and aggregation hierarchies.

• Automate business data feeds from source systems and build data marts that support strategic commercial decisions.

• Partner with platform engineering on the Google Cloud landing zone infrastructure as code CI/CD observability and cost guardrails.

• Significant commercial data engineering experience including time technically leading a team or workstream.

• Strong Python and SQL.

• Hands-on experience building batch and near-real-time pipelines on a cloud platform ideally Google Cloud (e.g. BigQuery Dataflow Pub/Sub Composer/Airflow).

• Strong data modelling skills including joining and reconciling data from multiple sources.

• Experience implementing data quality freshness and completeness monitoring.

• Experience with infrastructure as code (e.g. Terraform) and CI/CD for data pipelines.

• Clear communication with client stakeholders and experience mentoring engineers.

• Experience building feature pipelines for ML models.

• Tools such as dbt or experience working with high-volume device or telemetry data.

• Background in telecoms or telemetry data.

• Experience in client-facing consultancy or managed delivery environments.

At Zinkworks we are deeply committed to fostering a culture of diversity inclusion and belonging. We believe that our strength lies in the unique backgrounds perspectives and experiences of our team members. By embracing an inclusive environment we empower innovation and collaboration across all levels of our organisation. Based in Ireland we are proud to support and engage with our local communities through meaningful initiatives that promote equity and opportunity. Our commitment extends beyond the workplace as we actively contribute to creating a more inclusive and connected society for all.