Data Engineer

eComplete Group; Manchester, Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

eComplete Group is seeking a Data Engineer to own the build, deployment, and ongoing operation of our managed data platform across multiple client brands. You will work across the full data stack, from source connection through to the AI-powered query layer, delivering analytics-ready data and business insights.

This hands-on role requires strong SQL, data modelling, and experience with cloud data platforms (BigQuery, GCP) and AI tooling to drive value for brand operators and investors.

Qualifications

• Extensive SQL and data modelling experience for analytics-ready data models.
• Experience with cloud data platforms (GCP) and end-to-end ELT pipelines.
• Familiarity with AI-assisted analytics and data governance.

Responsibilities

• Deploy and manage cloud data infrastructure (GCP/BigQuery, IAM, Cloud Storage) for new client onboardings from the ground up.
• Build and maintain end-to-end data pipelines from connectors to analytics-ready output.
• Develop multi-layer SQL transformation models powering reporting and AI-driven queries.
• Produce client-facing analyses including funnel, LTV, and channel attribution.
• Support due diligence by structuring analytical output and QAing numbers for stakeholders.
• Extend AI-integrated workflows and data pipelines feeding Claude-powered outputs.
• Own platform health, monitor pipelines, and enforce data governance across multi-client deployments.

Skills

SQL

Data modeling

BigQuery

Python

AI tooling

Tools

Fivetran

Airbyte

dbt

GCP

IAM

Job description

There may be occasional travel to Manchester or London for events and/or meetings

About eComplete

eComplete is a specialist growth partner focused on beauty, wellness, and nutrition D2C brands.

We invest in brands we believe can go global, and we deploy our own operating platform including data infrastructure, commercial strategy, and specialist execution teams to accelerate growth across our portfolio and external clients.

The Role

We're hiring a Data Engineer to own the build, deployment, and ongoing operation of our managed data platform across multiple client brands.

You'll work across the full data stack, from source connection through to the AI-powered query layer, deploying structured, repeatable data infrastructure that puts business intelligence directly into the hands of brand operators and investors.

This is a hands-on technical role with meaningful client exposure. You won't just build pipelines, you'll encode business logic into a context layer that makes AI genuinely useful on real commercial data. You'll deploy platforms for new brands, maintain and evolve existing ones, and help shape how we productise this capability as we scale.

What You'll Do

• Deploy and manage cloud data infrastructure (GCP/BigQuery, IAM, service accounts, Cloud Storage) for new client onboardings from the ground up
• Build and maintain end-to-end data pipelines from connector configuration and sync scheduling through to raw data validation and transformation into analytics-ready output
• Develop multi-layer SQL transformation models (staging → core → semantic layer) that power accurate, business-contextualised reporting and AI-drive querying
• Produce client-facing performance analyses including funnel reports, subscription cohort analysis, LTV modelling, RFM segmentation, and channel attribution
• Support commercial due diligence for PE acquisition targets by handling raw data ingestion, structuring analytical output, and QA'ing every number before it reaches a stakeholder
• Extend and improve AI-integrated workflows including MCP servers, prompt engineering, and structured data pipelines that feed Claude-powered analytical outputs
• Own platform health and evolution, monitoring pipeline integrity, adapting the context and SQL model libraries as client businesses grow, and enforcing data governance across multi-client deployments
• Act as the client-facing technical lead, running discovery workshops, delivering platform walkthroughs, and providing data-backed answers throughout the managed service period

What We're Looking For

• Expert SQL and data modelling; you build and maintain modular, analytics-ready data models (dimensional, star schema) that serve BI tools, AI agents, and automated reports.
• BigQuery preferred; Snowflake, Redshift, or Postgres transferable Cloud and pipeline fluency; comfortable navigating GCP (BigQuery, IAM, GCS, CLI) and managing end-to-end ELT pipelines using tools like Fivetran, Airbyte, or dbt; you handle connector config, sync scheduling, schema management, and failure handling without hand-holding
• Python and AI tooling; you write scripts to automate provisioning and data quality checks, and you use AI assistants (Claude, ChatGPT) as a genuine daily tool — comfortable with prompt engineering and clear-eyed about what it unlocks for a small, high-output team
• Clear, structured communication; you can explain a metric, a business rule, or a join pattern in plain English, and encode that logic into documentation an AI agent can interpret.
• You surface blockers and share progress without being nudged — we weight this as heavily as technical output
• Self-directed and able to ship; you won't always be handed a spec; sometimes you'll write it and build it. You manage your own priorities, flag risks early, and take work from ambiguous brief to production output independly

Nice to have

• Ecommerce data familiarity; you've worked with Shopify, GA4, Meta Ads, Klaviyo, Amazon, or similar DTC data sources, or you're the kind of person who can pick up new metric frameworks and source-level data quirks rapidly. You're aware of concepts like AOV, LTV, subscription churn, and attribution — even if you haven't lived in them daily

Why This Role

You'll have an unusual combination of breadth, ownership, and impact. Here, you own the full vertical for each client, from connector to context layer to report. You'll see the direct commercial impact of what you build, the platforms you deploy inform PE investment decisions, and the analytics you produce shape brand growth strategies.

This isn't a maintenance role. You'll be building things that didn't exist before, working directly with investors and brand operators, and shaping a product that's being deployed across a growing portfolio.