Data Engineer-Spark,Scala

Zorba AI; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Zorba AI in Bengaluru, India, is seeking an experienced Data Engineer (Spark/Scala) to design, build, and optimize large-scale data pipelines across on-premises and cloud environments. You will work with Spark, Databricks, Scala/PySpark, SQL, and Python to deliver robust data workflows.

Join a team driving data modernization, migrating legacy processes, and ensuring reliable data movement across heterogeneous systems, with Azure cloud integration priorities as workloads evolve.

Qualifications

• Experience with Spark/Databricks for large-scale data processing.
• Proficiency with SQL for ETL and data extraction.
• Strong Python programming skills for data engineering tasks.
• Experience with on-prem to cloud migrations and hybrid architectures.
• Ability to design, implement, and optimize robust data pipelines.

Responsibilities

• Design, develop, and maintain large-scale data pipelines using Spark, Databricks, Scala Spark, and PySpark.
• Build and support complex on-prem data workflows, including hybrid integration patterns.
• Integrate data across diverse file systems and formats (JSON, Parquet, CSV, Excel, Avro).
• Write efficient SQL for data extraction, transformation, and loading.
• Develop and maintain workflow orchestration using Airflow for reliable execution.
• Write production-grade Python code for data processing and tooling.
• Build and maintain tests for data pipelines and ensure data quality.
• Create and maintain clear technical documentation for pipelines and architecture.
• Troubleshoot pipeline failures and performance bottlenecks across multi-system environments.
• Collaborate with data science and analytics teams to support downstream consumption.
• Support Azure cloud integration as workloads evolve to cloud/hybrid models.

Skills

Spark

Databricks

Scala

PySpark

SQL

Python

Airflow

Azure

Cloud integration

Tools

Amazon S3

HDFS

Parquet

JSON

CSV

Job description

About The Role

We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on‑premises and cloud environments. This role requires deep hands‑on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high‑performance data pipelines. You will work extensively on complex on‑prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

Data Engineer (Spark/Scala)

We are seeking an experienced Data Engineer to design, build, and optimize complex data workflows across on‑premises and cloud environments. This role requires deep hands‑on expertise in Apache Spark, Databricks, and Scala/PySpark, along with strong SQL and Python skills, to build robust, high‑performance data pipelines. You will work extensively on complex on‑prem workflows, integrating data across multiple file systems and formats, migrating and modernizing legacy processes, and ensuring efficient, reliable data movement across heterogeneous environments.

Key Responsibilities

• Design, develop, and maintain large‑scale data pipelines using Apache Spark, Databricks, Scala Spark, and PySpark
• Build and support complex on‑premises data workflows, including migration/hybrid on‑prem-to-cloud integration patterns
• Integrate data across diverse file systems (on‑prem file shares, NAS, HDFS, S3) and formats JSON, Parquet, Fixed‑Length, CSV, Excel, Avro
• Write efficient, optimized SQL for data extraction, transformation, and loading across relational databases
• Connect to and extract data efficiently from various source databases, tuning queries and pipelines for performance at scale
• Develop and maintain workflow orchestration using Airflow (or similar schedulers) for reliable, monitored pipeline execution
• Write clean, production‑grade Python code for data processing, automation, and tooling
• Build and maintain unit/integration tests for data pipelines to ensure data quality and reliability
• Create and maintain clear technical documentation for pipelines, data flows, and system architecture
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data quality issues in complex, multi‑system workflows
• Collaborate with cross‑functional teams (data science, analytics, application engineering) to support downstream data consumption
• Support cloud integration efforts, particularly with Azure, as workloads evolve from on‑prem to hybrid/cloud architectures

Required Qualifications

Primary Skills

• Strong hands‑on experience with Apache Spark and Databricks for large‑scale data processing
• Proficiency with Amazon S3 for data storage and pipeline integration
• Strong SQL skills - query optimization, complex joins, performance tuning
• Proven experience integrating data across various file systems and formats: JSON, Parquet, Fixed‑Length, CSV, Excel, Avro, etc.
• Strong knowledge of Scala Spark and PySpark for distributed data processing
• Strong Python programming skills for scripting, automation, and data engineering tasks
• Strong experience connecting to and efficiently extracting data from databases (relational/other), including performance‑conscious extraction strategies
• Demonstrated experience working on complex on‑prem data workflows (multi‑system integration, legacy system data extraction, hybrid on‑prem/cloud pipelines)
• Experience leveraging coding assistant tools and implementing AI agents to enhance development productivity and task execution.

Secondary Skills

• Experience with Azure cloud services (storage, compute, data services)
• Experience with Apache Airflow for workflow orchestration and scheduling
• Experience writing automated tests for data pipelines (unit, integration, data quality checks)
• Strong documentation skills able to clearly document pipelines, data lineage, and technical designs

Good To Have

• Working knowledge of Java
• Familiarity with React for building internal tooling/dashboards
• Experience with Prefect for workflow orchestration
• PBM (Pharmacy Benefit Management) / Healthcare domain knowledge

Data Engineer (Spark/Scala)

Good To Have

• Working knowledge of Java
• Familiarity with React for building internal tooling/dashboards
• Experience with Prefect for workflow orchestration
• PBM (Pharmacy Benefit Management) / Healthcare domain knowledge.

Data Engineer (Spark/Scala)

Good To Have

• Working knowledge of Java
• Familiarity with React for building internal tooling/dashboards
• Experience with Prefect for workflow orchestration
• PBM (Pharmacy Benefit Management) / Healthcare domain knowledge

Skills: scala,pipelines,data,spark