Data Engineer (SC Cleared)

Apache Spark Python AWS Cloud Data Pipelines

A hands-on data engineering role within a large-scale cloud data programme, responsible for building, maintaining, and troubleshooting data pipelines using Apache Spark, PySpark, Apache Airflow, and a broad suite of AWS services. You will apply strong analytical and engineering skills to deliver trusted, well-governed data assets in a modern, cloud-native environment.

About Scrumconnect

Scrumconnect is a leading UK technology consultancy delivering digital transformation across public and private sectors, contributing to over 20% of the UK's major citizen-facing public services. We specialise in cloud engineering, data platforms, and agile delivery, helping clients build scalable, secure, and user-centred digital solutions that create real impact.

Active SC clearance is a mandatory, non-negotiable requirement. Candidates must hold current, in-date Security Check (SC) clearance at the time of application. Sponsorship is not available. Applications without active SC clearance will not be considered.

Working arrangement:

This role is hybrid. Candidates must be willing and able to travel to the Newcastle office three days per week. Remaining days may be worked remotely from anywhere in the UK.

About the role

You will work as a Data Engineer on a complex, cloud-based data programme - designing, building, and maintaining data pipelines that process large volumes of data across a modern AWS-native stack. Using Apache Spark and PySpark for distributed data processing, Apache Airflow for orchestration, and a range of AWS services for storage, compute, and analytics, you will help deliver reliable, well-governed data assets to downstream users.

You will apply strong data analysis skills to identify root causes of data issues, work with dimensional data models and slowly changing dimensions, and implement infrastructure as code using Terraform. Familiarity with engineering best practices and the ability to translate customer expectations into applied technical functionality are key to success in this role.

Key responsibilities

Data pipeline development

Build and maintain scalable data pipelines using Apache Spark and PySpark, processing and transforming large datasets across distributed cloud infrastructure.

Workflow orchestration

Configure and manage Apache Airflow DAGs for task orchestration, ensuring reliable scheduling, monitoring, and execution of data processing workflows.

Root cause analysis

Perform data analysis to identify and resolve root causes of pipeline failures and data quality issues - including reviewing EMR output logs and CloudWatch metrics.

Data modelling

Apply understanding of dimensional data models and slowly changing dimensions (SCD) to design and maintain well-structured, analytically trusted data assets.

Infrastructure as code

Provision and manage cloud infrastructure using Terraform. Containerise solutions using Docker and manage deployments through GitLab CI/CD pipelines and release tagging.

Security & encryption

Apply understanding of both Server Side and client-side encryption patterns within AWS. Work within IAM policies and data governance standards appropriate to a regulated government environment.

Technical skills required

Languages & analytics

Python - primary language for pipeline development and data processing

SQL - used for querying, transformation, and validation across data stores

PySpark - for distributed data processing using Apache Spark on AWS EMR

Familiarity with basic data structures for constructing robust, scalable solutions

Data processing & orchestration

Apache Spark - understanding of distributed data processing architecture and execution

Apache Airflow - configuring DAGs and managing task orchestration at scale

Jupyter Notebooks - for exploratory data analysis and pipeline prototyping

Understanding of dimensional data models and slowly changing dimensions (SCD Types 1, 2, 3)

Data analysis skills to identify root cause of issues within pipelines and data assets

AWS services

Amazon EMR - running Spark workloads and reviewing output logs

Amazon Athena - ad hoc querying of data in S3

Amazon Textract and Comprehend - familiarity with AI/ML document extraction and NLP services

AWS S3, IAM, CloudWatch, EC2, ECR - core platform services used day-to-day

AWS console proficiency - navigating, configuring, and monitoring services

Understanding of Server Side and client-side encryption within AWS

Infrastructure, DevOps & delivery

Terraform - Infrastructure as Code for provisioning and managing AWS environments

Docker - containerisation of data engineering solutions

GitLab - source code management, CI/CD pipeline configuration, release tagging, and component versioning

Familiarity with engineering best practices

Ability to translate customer expectations into applied, functional technical solutions

Technology stack at a glance

Python

PySpark

SQL

Apache Spark

Apache Airflow

Jupyter Notebooks

Dimensional modelling/SCD

AWS EMR

Amazon Athena

AWS S3

AWS IAM

AWS CloudWatch

AWS EC2/ECR

Amazon Textract

Amazon Comprehend

Terraform

Docker

GitLab CI/CD

GitLab Tags