Senior Analytics Engineer Experimentation

N.B.!!! Location - remote from Latvia, Lithuania (we provide support).
 

In a partnership with one of the global consulting companies, we are looking for a Data Engineer for Experimentation. You will play a pivotal role in shaping, maintaining, and optimising the datasets and pipelines that power our client's (UK-based telecommunication company) experimentation capabilities. Working at the intersection of data engineering and analytics, you’ll collaborate closely with experimentation leads, analysts, Data products and product teams to deliver high-quality, trusted data assets that enable data-driven decisions across the organisation.

You will work primarily in AWS environments, building scalable and maintainable data workflows using SQL, and ensuring that data pipelines and models are efficient, standardised, and well-documented. This role focuses on data excellence and enablement, not infrastructure management, ideal for a data professional passionate about building reusable, high-quality data foundations for experimentation.

Key Responsibilities

·      Design, build, and maintain robust data pipelines and workflows in AWS to support experimentation datasets and metrics.

·      Transform, model, and standardise raw data into trusted, analysis-ready datasets using best practices in data modelling and governance.

·      Collaborate with data scientists, analysts, and product teams to translate business requirements into scalable data models and reliable datasets for experimentation.

·      Own and optimise SQL-based ETL processes for efficiency, reliability, and consistency across experiments.

·      Ensure data quality through testing, validation, and monitoring frameworks.

·      Document data flows, schemas, and pipeline logic for transparency and maintainability.

·      Partner with Analytics and Experimentation teams to design experiment tracking data structures and pipelines that capture key performance metrics.

·      Contribute to the continuous improvement of data standards, naming conventions, and reusable frameworks across the product data domain.

·      Coach and mentor colleagues on data modelling, pipeline best practices, and experimentation data principles.

Skills and experience required

·      Significant experience in a Data or Analytics Engineering role, preferably supporting digital product experimentation or data science workflows. (3+ years)

·      Advanced SQL expertise for building and optimising complex queries, transformations, and data models. (3+ years)

·      Strong knowledge of data modelling techniques (e.g. STAR schemas, 3NF, entity-relationship modelling, Medallion architecture).

·      Proven experience designing and maintaining data pipelines in AWS (e.g. Redshift, S3, Glue, Lambda, Step Functions, or similar).

·      Experience using modern data orchestration and transformation tools such as DBT, Airflow, or similar.

·      Ability to break down ambiguous business problems into clear, actionable data solutions.

·      Strong analytical and problem-solving skills, with the ability to architect scalable, efficient, and transparent data solutions.

·      Excellent communication skills, capable of working effectively with both technical and non-technical stakeholders.

·      Familiarity with experimentation frameworks (e.g., A/B testing pipelines, event tracking) is a strong plus.