Scala Data Engineer (Scala with AWS)

Role & responsibilities

Section

Details / Example Content

Job Requirements*

We are seeking an experienced Scala / Pyspark associate to design, develop, and maintain scalable data solutions on the Amazon Cloud Platform (AWS). The ideal candidate will have strong expertise in building modern data pipelines, data warehousing, big data processing, and cloud-native analytics solutions. The role requires close collaboration with business stakeholders, data architects, data scientists, and application teams to deliver reliable and high-performance data platforms.

Key Responsibilities*

• Design, develop, and optimize scalable data pipelines using scala and Pyspark in AWS environment.
• Build and maintain batch and real-time data ingestion and processing frameworks.
• Develop enterprise-grade data warehousing solutions using Scala and Pyspark.
• Analyze existing Scala and Spark applications and identify migration requirements.
• Convert Scala-based ETL, batch, and streaming pipelines into PySpark frameworks.
• Optimize PySpark jobs for performance, scalability, and resource utilization.
• Support cloud modernization initiatives on AWS/Databricks/Snowflake platforms.
• Implement ETL/ELT processes for structured and unstructured data.
• Integrate data from multiple sources including databases, APIs, files, and streaming platforms.
• Ensure data quality, governance, security, and compliance across data platforms.
• Automate deployment and operational processes using CI/CD and Infrastructure as Code (IaC).
• Monitor data pipelines and troubleshoot production issues.
• Collaborate with Data Architects, Business Analysts, and Data Scientists to translate business requirements into technical solutions.
• Implement data models, metadata management, and data lineage best practices.
• Support migration of on-premises or multi-cloud data platforms to AWS.
• Lead the migration, modernization, and optimization of Scala and Apache Spark workloads on Cloud environment, including conversion of Scala-based Spark applications to PySpark, performance tuning, cluster optimization, dependency management, and ensuring scalable, cost-effective, and resilient data processing solutions.

Required Technical Skills

• Scala
• PySpark
• AWS Glue
• AWS S3
• Step Functions
• Glue
• Lambda
• Pub/Sub
• Python
• SQL (Advanced)
• Event Bridge
• ECS
• EKS
• Snowflake

Section IV - Job Qualifications & Skills

Section

Details / Example Content

Domain

CMTS

Soft Skills

• Excellent communication

• Team collaboration

• Documentation and knowledge sharing

Education Requirements

Bachelor’s/masters in computer science or equivalent (Preferred)

Certifications

AWS Cloud Data Engineer (Preferred)

Snow pro certifications

Section V - Sample Questions for Training Model

Technical Question

Expected Answer

What to Evaluate

1.How does Scala code run on the JVM, and what JVM issues should a Scala developer understand?

Scala is compiled to JVM bytecode. A senior candidate should understand heap and stack behavior, garbage collection, boxing, class loading, JIT compilation, thread dumps, heap dumps, and Java library interoperability. They should also know that some high-level Scala constructs may create allocations or additional generated classes.

JVM production experience rather than only Scala syntax. Ask which diagnostic tools they used and how they interpreted results.

2.How would you migrate a large application from Scala 2 to Scala 3?

Assess compiler and library compatibility, upgrade to a suitable Scala 2.13 version, remove deprecated features, reduce macro and implicit complexity, validate cross-building support, migrate incrementally, and run complete automated tests and performance benchmarks. Special attention is required for macros, implicits, reflection, syntax changes, and third-party dependencies.

Real migration experience, dependency assessment, phased planning, backward compatibility, testing, and rollback strategy.

1. How do you determine the appropriate number of partitions?

Consider data volume, file sizes, cluster cores, operation type, shuffle volume, compression, and target output size. There is no universal partition count. The decision should be validated using task duration, partition-size distribution, available parallelism, spill, and Spark UI metrics.

Whether the candidate gives …