Python Developer with Pyspark

N Consulting Ltd
3 months ago
Applications closed

Related Jobs

View all jobs

Python/Data Science Developer

Data Engineer - Python & Azure

Geospatial Data Analyst

Data Analyst/Engineer, London

Python Developer Anaconda - MedTech

Python Developer

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.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 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.