Job
Description
Experience: Min 6+ Years
Job Title:
Data Engineer – Real-Time Streaming & Integration (Apache Kafka)
Location:
Bhopal, Madhya Pradesh
On-site role with opportunities to work on enterprise-scale data platforms
Note: Resource working on site will be provided with accommodation, lunch, and dinner by the
client for the complete project duration. The working week is 6 days (Monday – Saturday).
Role Overview:
We are seeking a highly skilled and experienced Data Engineer with 6+ years of
experience in designing and implementing real-time data processing pipelines and
streaming integrations. This role is ideal for professionals with deep expertise in
Apache Kafka, Kafka Connect, and modern ETL/ELT processes.
As a Data Engineer, you will play a critical role in building and optimizing data
integration frameworks to support large-scale, low-latency, and high-throughput data
platforms across enterprise systems. Your contributions will directly impact data
accessibility, business intelligence, and operational efficiency.
Key Responsibilities:
Design, develop, and maintain real-time streaming data pipelines using
Apache Kafka and Kafka Connect.
Implement and optimize ETL/ELT processes for structured and semi-structured
data from various sources.
Build and maintain scalable data ingestion, transformation, and enrichment
frameworks across multiple environments.
Collaborate with data architects, analysts, and application teams to deliver
integrated data solutions that meet business requirements.
Ensure high availability, fault tolerance, and performance tuning for streaming
data infrastructure.
Monitor, troubleshoot, and enhance Kafka clusters, connectors, and consumer
applications.
Enforce data governance, quality, and security standards throughout the pipeline
lifecycle.
Automate workflows using orchestration tools and CI/CD pipelines for
deployment and version control.
Required Skills & Qualifications:
Strong hands-on experience with Apache Kafka, Kafka Connect, and Kafka
Streams.
Expertise in designing real-time data pipelines and stream processing
architectures.
Solid experience with ETL/ELT frameworks using tools like Apache NiFi,
Talend, or custom Python/Scala-based solutions.
Proficiency in at least one programming language: Python, Java, or Scala.
Deep understanding of message serialization formats (e.g., Avro, Protobuf,
JSON).
Strong SQL skills and experience working with data lakes, warehouses, or
relational databases.
Familiarity with schema registry, data partitioning, and offset management in
Kafka.
Experience with Linux environments, containerization, and CI/CD best
practices.
Preferred Qualifications:
Experience with cloud-native data platforms (e.g., AWS MSK, Azure Event
Hubs, GCP Pub/Sub).
Exposure to stream processing engines like Apache Flink or Spark Structured
Streaming.
Familiarity with data lake architectures, data mesh concepts, or real-time
analytics platforms.
Knowledge of DevOps tools like Docker, Kubernetes, Git, and Jenkins.
Work Experience:
6+ years of experience in data engineering with a focus on streaming data and
real-time integrations.
Proven track record of implementing data pipelines in production-grade
enterprise environments.
Education Requirements:
Bachelor’s or Master’s degree in Computer Science, Information Technology,
or a related field.
Certifications in data engineering, Kafka,
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