Executive – Risk Tech (PySpark Developer), Regulatory & Financial Risk
Deloitte — Bengaluru, Karnataka
₹7–15 LPA (HireSetu estimate) · 0–2 yrs exp · Freshers eligible · Full-time
HireSetu listing reference 04448c — confirm requirements below, then apply on the employer site.
Quick answer: One non-negotiable: hands-on PySpark with real project delivery, at 0-2 years. The posting also carries a Python backend profile and a DevOps profile, neither of which is this requisition.
From Deloitte's job posting
Job Title: Executive | Risk Tech | Bengaluru Eco space | Regulatory & Financial Risk
Executive | Risk Tech | Bengaluru Eco space | Regulatory & Financial Risk
- Job requisition ID : 110871
- Location: Bengaluru Eco space
- Entity: Deloitte Touche Tohmatsu India LLP
The team
Deloitte Strategy, Risk & Transaction helps entities mitigate risk while discovering new opportunities to create value. Our end-to-end risk services span all domains, from managing strategic risks in the C-Suite to improving board oversight, and from balancing financial and environmental policies to addressing cyber threats. Learn more about Risk, Regulatory & Forensic)
Your work profile
We are looking for a skilled Senior Developer with strong expertise in PySpark to design, build, and maintain scalable data processing solutions.
The ideal candidate will have experience working across the data engineering stack, strong knowledge of distributed computing, and hands-on exposure to SDLC-based project development.
We are seeking an experienced Python Backend Developer to design, build, and maintain scalable backend systems and APIs.
The ideal candidate has strong expertise in Python frameworks, database design, and backend performance optimization.
We are looking for a DevOps Engineer to build, automate, and maintain scalable, secure, and highly available infrastructure.
The ideal candidate has strong experience in cloud platforms, CI/CD automation, and modern DevOps practices.
Key Responsibilities
Design, develop, and optimize large-scale data pipelines using PySpark.
Build and maintain ETL/ELT workflows to process structured and unstructured data.
Work with big data technologies such as Hadoop ecosystem, Spark, and Impala.
Collaborate with cross-functional teams including data scientists, analysts, and product teams.
Ensure code quality through best practices, code reviews, and version control.
Optimize job performance, memory management, and execution time in Spark jobs.
Work on data modeling, transformation, and pipeline orchestration.
Participate in end-to-end SDLC activities including requirement analysis, design, development, testing, and deployment.
Troubleshoot and resolve data processing issues and production incidents.
Contribute to automation, monitoring, and logging frameworks.
Required Skills and Qualifications
Strong hands-on experience with Python and PySpark (mandatory).
Strong SQL skills including advanced querying and performance tuning.
Experience with distributed data systems such as Hadoop and Spark ecosystem.
Experience with Impala, Hive, and SQL Server or other relational databases.
Strong understanding of data structures, algorithms, and performance optimization.
Experience with ETL pipeline development and data warehousing concepts.
Knowledge of SDLC methodologies (Agile/Scrum preferred).
Familiarity with version control systems such as Git.
Experience working with large datasets in production environments.
Knowledge of C# or other programming languages.
Experience with cloud platforms such as Azure, AWS, or GCP.
Exposure to workflow orchestration tools like Airflow or Oozie.
Understanding of data lakes, data governance, and data quality frameworks.
Experience with CI/CD pipelines.
Strong analytical and problem-solving abilities.
Good communication and collaboration skills.
Ability to work independently and in a fast-paced environment.
Proactive mindset with attention to detail.
Preferred Candidate Profile
0-2 Years of Hands-on PySpark developer with real-time project delivery experience.
Bacheler’s degree in any specialization
Exposure to end-to-end data pipeline lifecycle.
Comfortable handling large-scale data processing and optimization challenges.
Text from Deloitte's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
HireSetu's note
The bar is shipped PySpark
Python and PySpark are the only two skills marked mandatory, and the preferred profile asks for 0-2 years of hands-on PySpark with real-time project delivery. That is narrower than it sounds. They want pipelines you built and ran, not a notebook you followed along with. A fresher with a Spark capstone is competing against people who have spent six months on a live pipeline, so put whatever you have actually run at the top of your CV.
SQL is listed separately, with advanced querying and performance tuning attached. Tuning mentioned twice in one posting is not decoration.
Three jobs pasted into one ad
The work profile describes a senior PySpark developer, then a Python backend developer, then a DevOps engineer. The skills tags wander from Airflow to clinical research and FDA regulations, which reads like another team's requisition. The responsibilities and the preferred profile are the real signal: Spark pipelines, ETL/ELT, Hadoop, Hive, Impala, SQL Server. Treat the cloud and CI/CD lines as the wider stack you might touch over time, not day-one expectations for someone with under two years.
Most of the week goes on slow jobs
The responsibilities keep circling back to performance: job execution time, memory management, optimizing pipelines. In practice, much of the week goes on pipelines that already run but run badly, and the Spark UI is where you find out why. Partitioning, shuffle, joins and data skew are worth revising before any PySpark screen, and they come up everywhere, not only here.
You would be at Ecospace in Bengaluru, on the Outer Ring Road, so the commute is a real factor. Confirm the on-site expectation with the recruiter, since Deloitte India teams vary by project.
Drafted with AI from Deloitte's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.
How to apply
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