Data Engineer: Analytics

Role Overview

You will own the technical path from messy source data to clear, client-ready insight. This means designing pipelines and data models, validating the numbers yourself, and then walking clients through what the results actually say about their business. Because you face clients directly, you need equal comfort with SQL logic and with explaining that logic in plain language.

Key Responsibilities

• Architect and build ingestion, transformation, and modelling workflows that feed client-facing reporting.
• Take full ownership of data models, from choosing grain and keys to handling relationships, history, and automated tests, and document every design decision.
• Examine source data for quality issues, clean it, reconcile it against trusted records, and keep it reliable as the underlying data changes.
• Construct an analytics layer that directly answers client questions, then interpret what those answers mean for operational or commercial decisions.
• Present technical designs, analytical findings, and recommendations to client stakeholders across all levels of technical fluency.
• Support the pre-sales process by contributing to scoping conversations and helping shape realistic engagements.
• Challenge incomplete requirements, ask the right clarifying questions, and proactively surface risks before they become reporting failures.

Requirements & Qualifications

• At least three years of experience maintaining production data pipelines that other teams or departments depended on, exclusive of academic or personal projects.
• A current cloud certification specifically in a data, analytics, or machine learning track. Certifications outside this scope do not satisfy the requirement.
• Strong preference for Snowflake experience, especially SnowPro Core certification and deploying machine learning to production.
• Advanced SQL capability including window functions, CTEs, set logic, grain-aware aggregation, and the ability to read query plans and diagnose performance issues.
• Deep relational modelling knowledge: normalisation, surrogate vs natural keys, slowly changing dimensions, and clear judgment about when denormalisation is appropriate.
• Hands-on dbt experience in production, including incremental models, snapshots, and tests, with the ability to design project structure from scratch rather than inherit it.
• Practical Python skills for data analysis, process automation, and acquiring data from databases, APIs, flat files, and sites without a public interface.
• A disciplined view of data cleaning: profiling sources, reconciling them to systems of record, and maintaining standards as upstream data evolves.
• Quantitative reasoning skills sufficient to validate results statistically, plus the poise to defend your design and analysis choices under direct questioning.
• Proven ability to turn an analytical result into a clear chart, a written finding, and a recommendation that a non-technical audience can act on.
• If AI tools contribute to your work, you must be ready to explain and defend every line you deliver; tools are an accelerator, not a substitute for mastery.

Your CV should show the specific databases, platforms, and tools you used directly, the scale of the data involved, and your personal contribution rather than your team's. Include at least one data model you designed, your reasoning for its grain, and what you intentionally left out. Name a time source data arrived incomplete, duplicated, or inconsistent, and explain how you made it trustworthy for reporting. Describe an analytics deliverable that answered a business question and how that insight was used. Give one example of a difficult query or performance problem you solved, not just a list of self-assessed skill levels. List certifications with the issuing body and current status.

What We Offer / Why Join

• Opportunities to work with leading global OEMs and their technology stacks.
• Continuous professional development, with certification costs covered and study actively supported.
• Clear career progression inside a fast-growing technology consulting firm where your client-facing skills can accelerate your path to senior or leadership roles.