Data Engineer - Analytics and Modelling

Data Engineer - Analytics and Modelling

London,United Kingdom

Contract

Skills

full data lifecycle

data architecture

cloud-based architectures

Responsibilities

• Lead the design and implementation of AWS-based data products that

leverage reusable datasets.

• Collaborate on creating scalable data solutions using a range of new

and emerging technologies from the AWS platform.

• Demonstrate AWS data expertise when communicating with

stakeholders and translating requirements into technical data

solutions.

• Manage both real-time and batch data pipelines. Our technology stack

includes a wide variety of technologies such as Kafka, AWS Kinesis,

Redshift, and DBT.

• Design and model data workflows from ingestion to presentation,

ensuring data security, privacy, and cost-effective solutions.

• Create a showcase environment to demonstrate data engineering best

practices and cost-effective solutions on AWS.

• Build a framework suitable for stakeholders with low data fluency. This

framework should enable easy access to data insights and facilitate

informed decision-making.

Requirements

• Expertise in the full data lifecycle: project setup, data pipeline design,

data modelling and serving, testing, deployment, monitoring, and

maintenance.

• Strong data architecture background in cloud-based architectures

(SaaS, PaaS, IaaS).

• Proven engineering skills with experience in Python, SQL, Spark, and

DBT, or similar frameworks for large-scale data processing.

• Deep knowledge of AWS services relevant to data engineering,

including AWS Glue, AWS EMR, Amazon S3, Redshift.

• Experience with Infrastructure-as-Code (IaC) using Terraform or AWS

CloudFormation.

• Proven ability to design and optimize data models to address data

quality and performance issues.

• Excellent communication and collaboration skills to work effectively with

stakeholders across various teams.

• Ability to create user-friendly data interfaces and visualizations that

cater to stakeholders with varying levels of data literacy.