Description

Key Skills: Apache Spark, Java, SQL, Hadoop, Apache Kafka, Spring Boot, Oracle, Shell Scripting, Data Analysis, Big Data

Good to Have Skills: Experience with NoSQL data models (MongoDB, Couchbase) for analytical use cases. Familiarity with in-memory caching technologies such as Redis or Couchbase. Proficiency in Python (PySpark) for supplementary data manipulation and analysis tasks. Knowledge of cloud-based data platforms (AWS, Azure, or GCP) and their data services. Exposure to CI/CD pipelines and DevOps practices for data engineering workflows. Experience with data governance frameworks and data quality management tooling. Familiarity with containerization technologies such as Docker or Kubernetes. Understanding of RESTful API design and microservices architecture patterns.

Roles & Responsibilities:

• Consult with users and clients to solve complex data-related issues through in-depth evaluation of business processes, data sources, and industry standards.
• Analyze large and diverse datasets from various sources to identify trends, patterns, and anomalies, providing critical input for business and technology initiatives.
• Develop and document data mapping specifications, transformation logic, and ingestion requirements for new data pipelines and systems.
• Consult with business clients to determine functional specifications for data-centric systems and provide ongoing operational support.
• Design and implement scalable data pipelines and batch/streaming workflows using Apache Spark, Spark Streaming, Hive, and Hadoop within enterprise big data ecosystems.
• Develop and maintain backend services and automation scripts using Java, Spring Boot, JPA, and Shell Scripting to support data processing and operational workflows.
• Build and manage event-driven data architectures leveraging Apache Kafka for real-time data ingestion and streaming use cases.
• Automate job scheduling and dependency management using Autosys; manage and optimize Oracle database objects and queries to support analytical workloads.
• Develop supporting interfaces and data visualization components using JavaScript to enhance data accessibility and reporting capabilities.
• Identify, communicate, and mitigate risks and impacts related to data quality, data governance, and the application of technology.
• Act as an advisor or coach to new or lower-level analysts and collaborate effectively as a team to achieve business objectives.
• Act as a Subject Matter Expert (SME) on data sources, data models, and analysis techniques for senior stakeholders and team members.

Experience Required: 9–12 years of relevant experience in data analysis and data engineering, preferably within the Financial Services or Banking industry.

Education: Bachelor's degree / University degree in a technical or business discipline (Computer Science, Information Systems, Engineering, Finance, or equivalent experience)

Key Skills

Education

Any Graduate