Piotr Koper

Part of the core Data Engineering team within JLR’s Data & AI Office (DAIO), focused on infrastructure, developer tooling, and engineering standards. Progressed from pipeline development and data products into platform engineering, taking on technical leadership across multiple working groups.

Data Pipelines & Products

• Designed, developed and delivered ETL and ELT data pipelines for high-volume data applications using Python and SQL, incorporating automated data testing to ensure continuous data quality in deployment
• Evaluated Data Contract standards through hands-on testing, including dbt integration — and made a merged contribution to the Open Data Contract Standard (ODCS) open source project, shipped in v3.1.0

CI/CD Working Group — Lead Own and maintain semantically versioned GitLab CI/CD pipeline templates across the Data & AI Office and wider organisation, acting as one of the primary merge request reviewers and approvers.

• Develop new templates and drive reusability, code quality, security, and consistency — enabling multiple teams across the enterprise to validate and deploy code quickly with enforced standards
• Refactored multiple existing pipelines into a modular structure of reusable components, default templates, and pipeline definitions — covering Docker image builds, container scanning, Cloud Run, Cloud Functions, and other deployment targets
• Led the Workload Identity Federation rollout, designing an authentication template that replaces long-lived credentials in CI/CD variables with short-lived OIDC tokens while remaining backward compatible with legacy Service Account Key projects — materially improving security posture across a high volume of daily pipeline runs
• Designed and delivered bespoke CI/CD pipelines for the Dagster and Astronomer Airflow orchestration platforms, enabling SaaS integration with automated, controlled deployments

Terraform Working Group — Lead Lead the Terraform working group, owning module standards, provisioning patterns, and infrastructure-as-code practices across the data platform.

• Build and maintain reusable Terraform modules that let teams provision cloud resources consistently via CI/CD, without bespoke infrastructure work per project
• Provisioned the Workload Identity Federation infrastructure — Pool, Provider, and bindings — underpinning keyless GitLab to GCP authentication across the organisation
• Refactored the Notifications System Terraform module end to end — Cloud Run services behind API Gateway, private Cloud SQL PostgreSQL with enforced SSL and automated backups, Memorystore Redis, and Secret Manager with least-privilege IAM access, all over private VPC networking

Docker Images Working Group — Lead Lead the working group responsible for maintaining and improving standardised Docker images stored in GCP Artifact Registry, used by the DAIO and other teams.

• Maintain and improve custom runtime images for GitLab runners with the required tooling and dependencies pre-installed — removing runtime install steps from CI jobs, simplifying pipeline definitions, and reducing pipeline execution time
• Keep images current and secure through regular base image version updates and package maintenance

Standards, Documentation & Best Practices Develop and maintain engineering standards, guides, and best practices adopted by technical and non-technical teams across the organisation — written as Docs as Code, versioned and reviewed alongside the code they describe.

• Authored end-to-end documentation for key platform initiatives — including the Workload Identity Federation architecture, implementation and migration guide, the Notifications System, Terraform modules, test projects, Dagster and Astronomer Airflow guides, CI/CD components and pipeline templates and others
• Primary developer of standardised project setup scripts in Bash (Linux) and PowerShell (Windows), paired with pre-commit hooks for linting, formatting, and validation — promoting consistent development practices across teams

Notable Projects

• Python Task Orchestrator Evaluation (Lead) — structured evaluation of two enterprise orchestration platforms, building end-to-end data pipelines covering data validation, CI/CD integration, event-triggered execution, cross-platform authentication, and integrations with GCP, dbt, Dataform, and Snowflake. The formal evaluation report was adopted as the technical recommendation to the Enterprise Technology Board
• Notifications System — core developer of a GCP-based notifications system routing messages to email, Slack, and Jira based on subscription behaviour and API calls during pipeline execution
• GitLab Instance Migration — part of the team that designed and provisioned the Kubernetes cluster, namespace, and Helm module for self-hosted GitLab runners during a full instance migration, managed entirely via Terraform
• Enterprise Data Platform Investigation — contributed to a cross-functional investigation with the Enterprise Architect into the target layout and usage patterns for JLR’s Enterprise Data Platform

Successfully selected for a competitive internal conversion programme, transitioning into data engineering from a non-engineering background. Completed the structured curriculum covering data pipelines, ETL and ELT patterns, SQL, and Python — formally completed May 2022.

This programme marked the start of a deliberate career transition into data engineering, building the foundations that underpinned subsequent platform and infrastructure work.

Cross-functional programme role managing the Capacity, Parts Availability and Timing assessment process across JLR vehicle programmes. Responsible for critical path planning, risk management, and stakeholder communication across Purchasing, Supply Chain and Programme teams.

Built several internal tools to address existing process gaps — including a redesigned CPAT Input Matrix to improve data control and an automated WIP file creation process saving approximately 12 hours per file. Also developed an eCAR data scrape tool integrated directly into the WIP workflow.

This role developed the stakeholder management, cross-functional leadership and data-driven decision making skills that directly underpin the working group leadership taken on in data engineering.

Over a decade of progressive logistics and operations management across major UK distribution and manufacturing environments, including Halfords (1.4M units/week), Aston Martin Lagonda, and DHL.

Consistently built tooling and reports to replace manual processes — a Logistics Performance Tool tracking volumes, hours and costs against budget; automated planning tools; and redesigned operational workflows. The throughput numbers and team sizes involved meant data quality and analytical rigour weren’t optional — they were how the operation ran.

Selected for a competitive DHL Head Start management programme from hundreds of UK and Ireland applicants, one of 28 accepted.

This background directly informs how I approach data engineering — the domain problems, the stakeholder relationships, and the instinct to automate anything done more than twice.