Python Developer with Pyspark

N Consulting Ltd
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Junior Data Engineer

Senior Data Engineer

Developer with Data Engineering focus

IT Data Engineer

Data Engineer

Job Title:Python Developer with PySpark

Location:Northompton

Job Type:Contract

About the Role:
We are seeking a skilled Python Developer with expertise in PySpark to join our dynamic team. The ideal candidate will have strong experience in building and optimizing large-scale data processing pipelines and a deep understanding of distributed data systems. You will play a key role in designing and implementing data solutions that drive critical business decisions.

Key Responsibilities:

  • Develop, optimize, and maintain large-scale data pipelines using PySpark and Python.
  • Collaborate with data engineers, analysts, and stakeholders to gather requirements and implement data solutions.
  • Perform ETL (Extract, Transform, Load) processes on large datasets and ensure efficient data workflows.
  • Analyze and debug data processing issues to ensure accuracy and reliability of pipelines.
  • Work with distributed computing frameworks to handle large datasets efficiently.
  • Develop reusable components, libraries, and frameworks for data processing.
  • Optimize PySpark jobs for performance and scalability.
  • Integrate data pipelines with cloud platforms like AWS, Azure, or Google Cloud (if applicable).
  • Monitor and troubleshoot production data pipelines to minimize downtime and data issues.

Key Skills and Qualifications:

Technical Skills:

  • Strong programming skills inPythonwith hands-on experience inPySpark.
  • Experience with distributed data processing frameworks (e.g., Spark).
  • Proficiency in SQL for querying and transforming data.
  • Understanding of data partitioning, serialization formats (Parquet, ORC, Avro), and data compression techniques.
  • Familiarity with Big Data technologies such as Hadoop, Hive, and Kafka (optional but preferred).

Cloud Platforms (Preferred):

  • Hands-on experience with AWS services like S3, EMR, Glue, or Redshift.
  • Knowledge of Azure Data Lake, Databricks, or Google BigQuery is a plus.

Additional Tools and Frameworks:

  • Familiarity with CI/CD pipelines and version control tools (Git, Jenkins).
  • Experience with orchestration tools like Apache Airflow or Luigi.
  • Understanding of containerization and orchestration tools like Docker and Kubernetes (preferred).

Experience:

  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
  • 5+ years of experience in Python programming.
  • 4+ years of hands-on experience with PySpark.
  • Experience with Big Data ecosystems and tools.

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.