How We Engage

Three ways we plug in.

Most agencies sell you a senior on the call, then send a junior to do the work. We don't operate that way. Whether you need engineers embedded with your team, a new platform built end-to-end, or an honest second opinion — the people you talk to are the people you'll work with.

Staff Augmentation → Platform Delivery → Audit & Remediation →

What We Cover

Fields we work in.

We stay in the data space — it's where our experience runs deepest. If a project calls for specialists outside that boundary, we engage them directly so you don't have to manage multiple vendors.

Data Engineering
Pipelines, ingestion, transformation, orchestration. The plumbing that makes everything else possible.
Data Architecture
Platform design, storage strategy, modelling, and the decisions that shape your data stack for years.
Business Intelligence
Semantic layers, reporting infrastructure, and dashboards that decision-makers can actually trust.
Data Analysis
Exploratory work, metric definition, and analytical frameworks that connect data to business outcomes.
Artificial Intelligence — growing focus
AI is where we're investing most of our attention right now. We help companies build the data foundations that make AI actually work — clean training data, feature pipelines, model serving infrastructure, and evaluation frameworks. Good AI starts with good data. That's exactly our lane.

We deliberately stay within the data domain — it's where 15 years of our experience sits. When a project touches adjacent areas (software engineering, security), we bring in trusted specialists rather than stretch beyond what we know. You get one point of contact; we handle the coordination.

01 / Engagement
Staff Augmentation · most-requested

Experienced engineers, embedded with your team

Our most-requested service. Every engineer we send has been personally interviewed by us — most we've worked with directly. They show up, they ship, and they don't disappear two weeks in.

We place experienced data engineers and architects inside your organisation for the duration of a project. They adopt your tooling, your standards, your standups, and your code review process. The engagement looks and feels like a senior hire — except the matching, retention, and quality assurance are handled by us.

Each engineer is selected for the specific shape of the work. Before we propose anyone, we run a short discovery call with your team to understand your stack, the problem domain, and the seniority of the people they'll be working with. We then propose a single named candidate — not a stack of CVs to filter — and arrange a working interview so you can validate the fit yourself.

Typical engagements
  • Augmenting an in-house data platform team during a major migration or new initiative
  • Bridging a gap left by a senior departure while permanent hiring is in progress
  • Bringing specialised expertise (Snowflake, Databricks, dbt, Spark, streaming) for a defined phase
  • Standing up a brand-new data function alongside a first analytics or engineering hire
What's included
  • A personally interviewed engineer or architect, matched to the work
  • A working-interview round so your team validates fit before commitment
  • A weekly check-in with a SouthRivers Data partner — not the assigned engineer
  • Written status updates on what shipped, what's blocked, and what to decide next
  • Replacement guarantee: if it isn't working, we replace within two weeks at our cost
Engagement
3–12 months, renewable
Commitment
Full-time or part-time
Pricing
Monthly retainer
Notice
30 days, either side
02 / Engagement
Platform Delivery

Build a new data platform from scratch

If your current setup was designed for last year's company, we'll build the one you need for the next three. No reference architectures pulled off a vendor blog. Designed for your business, not a logo slide.

For companies that have outgrown spreadsheets, ad-hoc scripts, or a first-generation warehouse that no longer fits the volume or complexity of the business. We design and deliver a complete data platform — ingestion, storage, transformation, modelling, observability, and access controls — sized for where you are now and where you'll be in three years.

Every platform we build is shaped by the business it serves. We start with a short discovery to understand the operating model, the analytical questions that actually matter, and the constraints (regulatory, cost, team capacity) that should bound the design. The result is an architecture document you can defend in front of your board — followed by an implementation that delivers it.

What we deliver
  • Architecture document and decision log — every meaningful trade-off recorded with reasoning
  • Cloud infrastructure provisioned via code, with environment parity across dev/stage/prod
  • Ingestion, transformation, and modelling layers with versioning, testing, and lineage
  • Observability: data quality monitoring, freshness SLAs, cost dashboards, alerting
  • Documentation written for the team that will inherit it — not for the team that built it
  • Knowledge transfer over the final phase so your team can run and extend it without us
Stack we typically work in
  • Warehouses: Snowflake, Databricks, BigQuery
  • Transformation: dbt, Apache Spark, custom pipelines where they're warranted
  • Orchestration: Airflow, Dagster, Prefect
  • Streaming: Kafka, Kinesis, where the use case justifies it
Engagement
3–9 months
Team size
2–4 engineers
Pricing
Fixed-scope phases
Handover
Built into final phase
03 / Engagement
Audit & Remediation

Fix what's already there

You don't always need a rebuild. Sometimes you need someone senior to look at what you have and tell you the truth. We come in, we look hard, we write it up plainly. You decide what to do.

A structured review of an existing data platform, conducted by senior architects, resulting in a written assessment with a prioritised remediation plan. Useful when the platform feels off but the symptoms are unclear, when costs are rising without explanation, when reliability has degraded, or when leadership is weighing a rebuild against incremental fixes.

The audit is independent of the remediation. You're free to take the report and execute it with your own team, with another partner, or with us — whichever fits best. We charge a fixed fee for the audit and quote separately for any follow-on work, with no obligation either way.

What we examine
  • Architecture: pipeline design, data modelling, storage and compute choices, environment hygiene
  • Reliability: failure rates, recovery posture, observability, on-call burden
  • Cost: cloud spend breakdown, query patterns, storage footprint, optimisation opportunities
  • Velocity: time-to-deliver for new requests, blockers in the development cycle, technical debt
  • Team: capacity, skill coverage, knowledge concentration, onboarding maturity
What you receive
  • Written assessment, 20–40 pages, in plain language
  • Prioritised remediation plan with effort estimates and dependency mapping
  • An executive summary suitable for sharing with leadership or the board
  • A working session with your team to walk through the findings and answer questions
Engagement
3–5 weeks
Team
Senior architect + partner
Pricing
Fixed fee
Output
Written report & plan

How We Work

A few constants, regardless of engagement.

No bench-warmers
Every engineer we send is someone we'd put on our own project. If they wouldn't pass our bar, they don't pass yours.
No bait and switch
Whoever you meet on the call is who shows up to do the work. The good ones don't get pulled away mid-engagement.
Architects, not implementers
We don't fix one pipeline and call it strategy. We think about the whole platform — because that's where the real costs and savings live.
You own everything
Code, documentation, decisions, context. When we leave, you leave with the keys. No hostage situations, no lock-in.