3. Architecture & Data Modeling
Design structured, semi-structured, and unstructured data storage schemas using columnar file formats (e.g., Parquet, ORC, Avro).
Implement robust data validation, cleansing, data governance, and error-handling mechanisms across the ingestion lifecycle.
Ensure data privacy and enterprise compliance by applying encryption at rest/transit and access-control policies.
4. Collaboration, CI/CD & Best Practices
Participate in Agile/Scrum ceremonies, sprint planning, and code reviews to ensure adherence to high code quality standards.
Write comprehensive unit, integration, and automated regression tests using frameworks such as JUnit, Mockito, and Spark Testing Base.
Configure and maintain continuous integration and continuous deployment (CI/CD) pipelines using tools like Jenkins, GitLab CI, or GitHub Actions.
Required Qualifications & Skills
Technical Competencies
Core Java: Deep proficiency in Java (Java 8/11/17+), including multithreading, concurrency, OOP principles, memory management, and JVM internals.
Apache Spark: Hands-on experience developing distributed applications with Apache Spark (RDDs, DataFrames, Datasets, Spark SQL, Spark Structured Streaming).
Distributed Ecosystem: Strong working knowledge of distributed architecture (HDFS, YARN), Hive, and distributed storage systems.
Messaging & Streaming: Practical experience with event streaming platforms such as Apache Kafka or RabbitMQ.
Database & Query Languages: Advanced SQL capabilities, experience with relational databases (PostgreSQL, Oracle, MySQL) and NoSQL datastores.
Build & Version Control: Proficiency with build tools (Maven, Gradle) and Git version control workflows.
Testing: Solid track record in Test-Driven Development (TDD) using JUnit, Mockito, and distributed testing patterns.
Professional Experience & Education
Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Software Engineering, or a related technical discipline.
Experience: 3-6 years of professional software engineering experience, with at least 2–4 years dedicated to building scalable distributed data processing applications using Java and Apache Spark.
Preferred / Desired Qualifications
Cloud Platforms: Experience building and deploying data architectures on AWS (EMR, S3, Glue, Athena), Azure (Databricks, HDInsight, ADLS), or Google Cloud (Dataproc, BigQuery).
Modern Lakehouse Technologies: Hands-on exposure to Apache Iceberg, Delta Lake, or Apache Hudi.
Containerization & Orchestration: Familiarity with Docker, Kubernetes, and workflow schedulers like Apache Airflow or Luigi.
Polyglot Exposure: Familiarity with Scala or Python (PySpark) is an added advantage.
Job Family Group
Technology
Job Family
Applications Development
Time Type
Full time
- Most Relevant Skills
- Please see the requirements listed above.
- Other Relevant Skills
- For complementary skills, please see above and/or contact the recruiter.
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