Depth in the detail.
Perspective across the work.
More than 25 years in retail data and FMCG. Hands-on development, systems creation and responsibility for the production environment, alongside leading a small technical team.
A foundation built over time.
- Mar 2023 – presentCurrent role
Production Consultant
Circana
Hands-on technical leadership of Marketplace production, combining development, data delivery and responsibility for the operating environment.
- May 2015 – Mar 2023
Senior SQL Manager
IRI
Managed SQL Server development and automated retail-data production, combining hands-on technical delivery with team leadership.
- Feb 2010 – May 2015
Production Manager / SQL DBA & Developer
Litmus MR Limited
Managed production and SQL databases, and co-developed the SQL infrastructure for a longstanding retail data platform.
- Mar 2008 – Feb 2010
Production Manager
Synovate Aztec
Oversaw the data flow and production of EPOS data for over 30 retailers, using SQL Server 2005.
- 2001 – 2008
Production Operator
PS&A Ltd
Developed systems to format and standardise varied data sources. Created and maintained standard and bespoke FMCG reports across 130 categories.
Enterprise data engineering.
My strongest technical foundation is SQL Server and T-SQL, which I have worked with since 2008. I design and build stored procedures and functions, develop SSIS processes from scratch, tune performance and investigate the detail when data does not look right.
This is hands-on, daily engineering work. The processing involves millions of aggregated EPOS rows per retailer, per week, with the demands that brings for dependable workflows and a close understanding of the data.
I also create the applications and dashboards around that processing: quality checks, trend and latency views, live-versus-test comparisons and self-service controls.
Python and VBScript are regular tools in my day-to-day scripting and automation work. I also maintain an environment that includes Perl, where I have investigated and debugged problematic scripts.
The environment
behind the data.
My responsibility extends beyond individual pipelines to the day-to-day operation of the production environment. That includes investigating processing issues, monitoring storage capacity across server drives and shares, and keeping track of how the wider environment is behaving.
I work closely with IT and have been involved in operating-system and SQL Server version upgrades, including their impact on the processes that run around them.
Understanding the data, the jobs and the infrastructure together is a practical part of keeping delivery moving.
Leadership through
technical involvement.
I lead a small technical team while continuing to design, build and investigate the systems we run. Technical responsibility and team leadership are part of the same role.
Daily stand-ups help me spot blockers and gaps in understanding. I create walkthrough videos and written guides that colleagues can revisit, and direct people to the relevant resources in our shared operational index.
That guidance is used regularly, including beyond my immediate team. A brief pointer can replace another full walkthrough, while giving colleagues something they can work through again at their own pace.
AI, applied to
real engineering work.
I use AI-assisted development to create internal applications, reporting and investigation tools. The starting point is the underlying engineering: understanding the data, defining the logic and shaping a workflow around a real task.
Recognised comparison tooling.
I created a tool to investigate the impact of proposed retailer data restatements before publication. It compares live and test data, starting with the overall picture and allowing investigation through different product and market views, down to week, store and product detail.
Reusable meeting skills.
I developed all seven meeting skills created within our group in an early Claude pilot, contributing the ideas and their implementation. The pilot is exploring how reusable skills could form part of a wider company library.
My wider experience includes AI report tooling, Naive Bayes product attribution and AI-assisted Python and Flask applications.
Exploring local AI.
At home, I’ve set up and experimented with local models on Pop!_OS using an RTX 3090. That has involved understanding context windows, KV cache and the practical limits of the hardware, alongside considering when local or cloud-hosted models make sense.
Agoravista is one of the personal projects where I’ve put that curiosity into practice.
Explore AgoravistaData, in its
business context.
I have worked with EPOS data delivery since 2001, across retail, FMCG analysis and market research. That background includes standard and bespoke reporting for internal and external clients, alongside automated quality-control and data-delivery systems.
Understanding the commercial context is part of understanding the data: the categories, the retailer, the question behind a report and the detail someone needs to investigate.
A practical way of working.
Understand the detail.
Follow the data and investigate the cause. A clear explanation matters as much as a quick answer.
Make the work useful.
A dashboard, process or internal tool should help someone understand something or get something done.
Stay curious.
Long experience is a foundation to build on. There is always room to learn a new technique or ask a better question.
The people I’ve worked with.
A couple of excerpts from LinkedIn recommendations, shared in 2012 and 2013.
“He is always keen to help others and is a good team player.”
“Steve Reeve is an honest, careful and innovative Database Specialist.”
See the experience in context.
Explore a selection of the systems, investigations and tools behind the work.