Senior Data Engineer - Spark Streaming & Kafka

Job Description

About Kagool

\nWe are a fast-growing IT consultancy specializing in the transformation of complex Global enterprises that use SAP. We are looking for hard working individuals to help deliver for our global customer base. We embrace the opportunities of the future and work proactively to make good use of technology. As you can imagine, this means that we have a vibrant and diverse mix of skills and people making Kagool a great place to work.

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\nJob Summary

\nWe are looking for an experienced Senior Data Engineer with 7+ years of experience in designing, developing, and supporting scalable data engineering solutions.

\nThe ideal candidate must have strong hands-on experience with Apache Spark, PySpark, Kafka, Spark Structured Streaming, Python, SQL, and Microsoft Azure. The candidate should have practical experience implementing and managing Kafka-based streaming solutions on Azure and working with real-time data processing pipelines.

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\nAzure experience is mandatory for this role.

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\nKey Responsibilities

\n\n • Design, develop, and maintain scalable batch and real-time data pipelines using Apache Spark and PySpark.

\n • Develop and support Kafka-based real-time streaming pipelines on Microsoft Azure.

\n • Implement real-time data processing using Spark Structured Streaming.

\n • Work with Kafka topics, partitions, consumer groups, offsets, retention, and message delivery mechanisms.

\n • Implement Kafka producers and consumers for high-volume data ingestion and processing.

\n • Integrate Apache Kafka with Azure data services and downstream data platforms.

\n • Develop data transformation, cleansing, aggregation, and enrichment processes.

\n • Implement checkpointing, fault tolerance, error handling, retry mechanisms, and recovery strategies for streaming applications.

\n • Design and optimize Spark jobs for performance, scalability, and efficient resource utilization.

\n • Work with Azure data services such as Azure Databricks, ADLS Gen2, Azure Event Hubs, Azure Data Factory, and Azure Synapse Analytics.

\n • Develop SQL queries and work with relational and analytical databases.

\n • Implement data quality and validation frameworks.

\n • Troubleshoot production issues and perform root-cause analysis.

\n • Collaborate with Data Architects, Application Teams, DevOps, and Business stakeholders.

\n • Participate in technical design discussions, code reviews, and CI/CD activities.

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\nMandatory Technical Skills

\n\n • 7+ years of experience in Data Engineering / Big Data.

\n • Strong hands-on experience with Apache Spark and PySpark.

\n • Strong hands-on experience with Apache Kafka.

\n • Mandatory hands-on experience with Kafka on Azure.

\n • Mandatory Microsoft Azure experience.

\n • Strong experience with Spark Structured Streaming.

\n • Strong programming skills in Python.

\n • Strong SQL skills.

\n • Experience designing and developing real-time/streaming data pipelines.

\n • Good understanding of distributed computing and Big Data architecture.

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\nHands-on experience with Kafka

\n\n • Topics and partitions

\n • Producers and consumers

\n • Consumer groups

\n • Offsets

\n • Retention

\n • Partitioning

\n • Error handling and recovery

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\nStrong understanding of Spark concepts including

\n\n • Partitioning

\n • Shuffling

\n • Joins

\n • Caching

\n • Serialization

\n • Performance tuning

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\nHands-on experience with Azure Databricks and/or Azure data engineering services.

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\nExperience with Git and CI/CD practices.

\nAzure Skills

\nCandidates must have practical experience with one or more of the following

\nAzure Databricks

\nAzure Event Hubs

\nAzure Data Factory

\nAzure Data Lake Storage Gen2

\nAzure Synapse Analytics

\nAzure Functions

\nAzure Monitor

\nAzure Key Vault

\nMicrosoft Entra ID / Azure authentication

\nAzure Kafka Requirement

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\nThe candidate should have hands-on implementation/support experience with Kafka in an Azure environment, including experience with:

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\nKafka deployment/integration on Azure

\nKafka producers and consumers

\nKafka topics and partitions

\nConsumer groups and offset management

\nKafka-to-Spark streaming integration

\nMonitoring and troubleshooting Kafka workloads

\nPerformance tuning and scalability

\nSecurity/authentication for Kafka workloads on Azure

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\nGood to Have

\n\n • Delta Lake

\n • Delta Live Tables

\n • Kafka Connect

\n • Confluent Kafka / Confluent Cloud

\n • Schema Registry

\n • Avro

\n • Azure Event Hubs

\n • Kubernetes

\n • Terraform

\n • Azure DevOps

\n • Prometheus / Grafana

\n • CI/CD automation

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\nCandidate Profile

\nThe candidate should be comfortable working on large-scale; high-throughput streaming systems and should have experience taking data pipelines from design and development through production deployment and operational support.

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\nCareer at Kagool

\nA career at Kagool will give you a path towards progression and opportunities, with the current rate of growth we at Kagool have dedicated time towards individual growth, recognizing individual contributions, filling the team with a strong sense of purpose along with providing a fun, flexible and friendly work environment