Apply for the Data Analyst position at Barclays in Pune, India. Drive data quality, gain exposure to banking, and work with cutting-edge technology while contributing to business strategy.
Key Details of Data Analyst Role at Barclays
| Company Name | Barclays |
|---|---|
| Job Title | Data Analyst |
| Employment Type | Permanent |
| Location | Pune, India |
| Business Area | Customer Digital and Data |
| Area of Expertise | Data & Analytics |
| Contract | Permanent |
| Date Posted | Dec 30, 2024 |
| Reference Code | JR-0000026707 |
Overview of the Role
Join Barclays as a Data Analyst and be at the forefront of driving data-driven strategies in the banking domain. In this role, you will focus on data quality, data lineage, and data controls, helping transform business processes and enhance the customer experience.
As a Data Analyst, you will play a key part in designing data pipelines, analyzing large datasets, and applying machine learning to solve complex business problems. You will work closely with a team of developers, using advanced technologies to improve operations and deliver impactful business insights.
Responsibilities of the Data Analyst
- Investigate data issues related to quality, lineage, and authoritative sources.
- Execute data cleansing and transformation tasks to prepare data for analysis.
- Design and build data pipelines to automate data movement and processing.
- Develop advanced analytical techniques, including machine learning and AI, to address business challenges.
- Document data quality findings and provide recommendations for improvement.
- Collaborate with internal teams to implement data quality improvements and process optimizations.
Required Skills and Experience
- Banking domain knowledge, particularly in areas like collections and recovery, and lending.
- Strong SQL skills and experience with Unix Shell scripting.
- Proficiency in PySpark for data processing.
- Experience working on data warehousing projects.
- Knowledge of Data Modeling Tools and Big Data concepts.
- Additional technical skills: Python, Teradata, Oracle, Cloud Technologies, JIRA Board (Agile), Microsoft Office (Excel, PowerPoint).
Key Accountabilities
- Analyze data quality issues, identify root causes, and suggest improvements.
- Develop and implement automation solutions to enhance data processing efficiency.
- Work with business leaders to translate data insights into actionable business strategies.
- Lead or contribute to team-based data initiatives, ensuring alignment with organizational goals.
- Take ownership of data quality procedures, ensuring the accuracy and consistency of critical business data.
- Manage risk by adhering to internal processes and policies.
Expectations from the Data Analyst
- Deliver high-quality results within the expected timelines, continuously improving data processing and analysis methods.
- Demonstrate technical expertise and contribute valuable insights into the business processes.
- Collaborate across departments to solve complex data issues and ensure alignment with business goals.
- Help manage and mitigate business risks while ensuring compliance with data regulations.
Why Barclays?
- Inclusive Culture: Barclays fosters a culture of respect, inclusion, and empowerment, where diversity and innovation drive success.
- Global Reach: Barclays offers opportunities to collaborate with colleagues and clients around the world, providing unique experiences and learning opportunities.
- Flexible Working: Barclays supports flexible working arrangements, ensuring a healthy work-life balance for all employees.
- Learning & Development: Access to resources that support continuous learning, growth, and career advancement.
How Your Profile Fits This Role
If you have experience in data analysis, strong proficiency in SQL, PySpark, and a banking background, this role is a perfect match. Highlight your experience in data modeling, and machine learning to demonstrate your suitability for this position.
Tailor Your Resume:
- Emphasize your experience with data quality management, automation tools, and big data technologies.
- Highlight specific examples of banking domain experience, particularly in collections, lending, or data modeling.
1. Understand the Role and Requirements
- The job involves data quality, data cleansing, building data pipelines, and advanced analytics (including AI and machine learning). It also emphasizes banking domain knowledge (preferably collections, recovery, and lending) and technical proficiency in tools like SQL, Python, PySpark, and data modeling.
- Key responsibilities: investigation of data issues, designing and building data pipelines, applying advanced analytical techniques, and documentation.
2. Review Essential Skills and Concepts
Technical Skills:
- SQL: Brush up on writing complex queries, joins, subqueries, aggregations, and using window functions. Practice on platforms like LeetCode, HackerRank, or Mode Analytics.
- Python & PySpark: Familiarize yourself with Python data manipulation libraries (Pandas, NumPy) and data processing in PySpark.
- Data Warehousing: Understand the basics of data warehousing, ETL (Extract, Transform, Load), and OLAP cubes.
- Data Modeling: Learn about data modeling concepts like normalization, star schema, snowflake schema, and dimensional modeling.
- Unix Shell Scripting: Practice basic commands for file manipulation, data processing, and automating tasks.
Concepts:
- Data Quality & Cleansing: Know techniques for identifying and resolving data issues, such as handling missing values, outliers, and duplicate records.
- Data Pipeline Design: Learn how to build scalable data pipelines using Python or PySpark for automating data movement and processing.
- Machine Learning Basics: Understand basic machine learning algorithms (e.g., linear regression, decision trees, clustering) and their application in data analysis.
- Big Data Tools (e.g., Hadoop, Spark): Familiarize yourself with tools that handle large datasets, as Barclays might deal with big data.
Soft Skills:
- Communication: As a data analyst, you’ll need to communicate complex findings to stakeholders, so practice explaining technical concepts in simple terms.
- Problem Solving & Analytical Thinking: Prepare examples from your coursework or projects where you used analytical thinking to solve problems.
- Teamwork & Collaboration: Highlight instances where you worked in teams (such as on group projects) and contributed to shared goals.
3. Review Barclays’ Business and Culture
- Understand Barclays’ Values: Read about their values—Respect, Integrity, Service, Excellence, and Stewardship. Think of examples where you have demonstrated these values in your past experiences.
- Diversity and Inclusion: Barclays places emphasis on a diverse and inclusive workplace. Reflect on how you contribute to a positive and inclusive team environment.
4. Study the Interview Process
Barclays follows a structured interview process, including:
- Assessment: A test covering behavioral and technical skills.
- Interview: A mix of technical questions, situational judgment, and behavioral questions.
- Scenario-Based Questions: Be prepared for case studies or data problems related to the banking domain, like fraud detection, customer segmentation, or optimizing a lending process.
5. Prepare Case Studies and Practice
Practice Case Studies:
- Data Cleansing: Prepare a case where raw data needs to be cleaned (handling missing data, correcting inconsistencies).
- Data Pipelines: Practice designing a simple ETL pipeline and explain how you’d automate data flow.
- Business Analysis: Look for data-related business cases in banking (e.g., improving collections efficiency, customer retention strategies). Be ready to apply your data skills to solve such business problems.
Practice Problem Solving:
- Use websites like LeetCode, Hackerrank, or Kaggle to practice SQL and Python challenges that mimic real-world data analysis tasks.
6. Prepare for Behavioral Questions
- Be ready to discuss your academic background, projects, and any internships. Highlight:
- How you handled challenges or data issues.
- How you worked with teams to meet project deadlines.
- Your interest in the banking sector and how your skills can contribute to Barclays’ mission.
7. Mock Interviews and Feedback
- Conduct mock interviews with peers or mentors to simulate the interview environment. Practice technical questions and answering behavioral queries confidently.
8. Stay Up to Date with Industry Trends
- Familiarize yourself with emerging trends in data analytics, such as advancements in AI/ML, data privacy regulations, and how they might impact the banking industry.
9. Clarify Your Questions
- Prepare a list of insightful questions to ask the interviewer about team culture, data challenges at Barclays, and opportunities for growth in the data analytics domain.
Apply Now!
If you’re passionate about working with data in the banking domain and enjoy solving complex business problems, apply for the Data Analyst role at Barclays in Pune today!
