AWS Data Engineer

Stackstudio Digital Ltd.
London
1 month ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer

AWS Data Engineer Telecom Domain

AWS Cloud Data Engineer

Lead AWS Data Engineer

Lead AWS Data Engineer

Databricks SME and AWS Data Engineer

Job title: AWS Data Engineer
Location: Ipswich (Onsite- 5 days)
Type of Employment- Permanent
Job Overview:

We are seeking an experienced

AWS Data Engineer

with strong expertise in

ETL pipelines, Redshift, Iceberg, Athena, and S3

to support large-scale data processing and analytics initiatives in the

telecom domain . The candidate will work closely with data architects, business analysts, and cross-functional teams to build scalable and efficient data solutions supporting network analytics, customer insights, billing systems, and telecom OSS/BSS workflows.
Key Responsibilities

  1. Data Engineering & ETL Development
    Design, develop, and maintain

    ETL/ELT pipelines

    using AWS-native services (Glue, Lambda, EMR, Step Functions).
    Implement data ingestion from telecom systems like

    OSS/BSS, CDRs, mediation systems, CRM, billing, network logs .
    Optimize ETL workflows for large-scale telecom datasets (high volume, high velocity).
  2. Data Warehousing (Redshift)
    Build and manage scalable

    Amazon Redshift

    clusters for reporting and analytics.
    Create and optimize

    schemas, tables, distribution keys, sort keys , and workload management.
    Implement Redshift Spectrum to query data in S3 using external tables.
  3. Data Lake & Iceberg
    Implement and maintain

    Apache Iceberg

    tables on AWS for schema evolution and ACID operations.
    Build Iceberg-based ingestion and transformation pipelines using Glue, EMR, or Spark.
    Ensure high performance for petabyte-scale telecom datasets (CDRs, tower logs, subscriber activity).
  4. Querying & Analytics (Athena)
    Develop and optimize

    Athena

    queries for operational and analytical reporting.
    Integrate Athena with S3/Iceberg for low-cost, serverless analytics.
    Manage Glue Data Catalog integrations and table schema management.
  5. Storage (S3) & Data Lake Architecture
    Design secure, cost-efficient

    S3 data lake

    structures (bronze/silver/gold zones).
    Implement data lifecycle policies, versioning, and partitioning strategies.
    Ensure data governance, metadata quality, and security (IAM, Lake Formation).
  6. Telecom Domain Expertise
    Understand telecom-specific datasets such as:

    CDR, xDR, subscriber data
    Network KPIs (4G/5G tower logs)
    Customer lifecycle & churn data
    Billing & revenue assurance

    Build models and pipelines to support

    network analytics, customer 360, churn prediction, fraud detection , etc.
  7. Performance Optimization & Monitoring
    Tune Spark/Glue jobs for performance and cost.
    Monitor Redshift/Athena/S3 efficiency and implement best practices.
    Perform data quality checks and validation across pipelines.
  8. DevOps & CI/CD (Preferred)
    Use

    Git, CodePipeline, Terraform/CloudFormation

    for infrastructure and deployments.
    Automate pipeline deployment and monitoring.
    Required Skills
    3 10 years

    experience in data engineering.
    Strong hands-on experience with:

    AWS S3, Athena, Glue, Redshift, EMR/Spark
    Apache Iceberg
    Python/SQL

    Experience in

    telecom data pipelines

    and handling large-scale structured/semi-structured data.
    Strong problem-solving, optimization, and debugging skills.
    Good to Have Skills
    Knowledge of

    AWS Lake Formation ,

    Kafka/Kinesis ,

    Airflow , or

    Delta/Apache Hudi .
    Experience with ML workflows in telecom (churn, network prediction).
    Exposure to

    5G network data models .

    TPBN1_UKTJ

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.