Python Developer with Pyspark

N Consulting Ltd
8 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer - AVP Capital Markets

AWS Data Engineer - VP Capital Markets

AWS Data Engineer - VP Capital Markets

AWS Data Engineer - AVP Capital Markets

Principal Data Scientist

People Data Scientist

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.