Lead Data Engineer

Lead Data Engineer

Experience: 6–8 Years

Location: Bengaluru / Hybrid

Employment Type: Full-Time

Role Overview

We are looking for an experienced Lead Data Engineer with strong hands-on expertise in real-time data streaming, event processing, CDC, data integration, and modern data engineering.

The candidate will be responsible for designing, developing, and maintaining high-performance, production-grade data pipelines using Apache Flink, Apache Kafka, Debezium, CDC, ClickHouse, and Apache Airflow.

This is a hands-on engineering role requiring practical experience in building high-volume, low-latency streaming applications, developing scalable data pipelines, troubleshooting distributed systems, and optimizing data processing workloads.

The ideal candidate should be comfortable working across the complete data pipeline—from source systems and CDC ingestion through Kafka and Flink processing to analytical storage, APIs, dashboards, and downstream integrations.

Key Responsibilities

1. Real-Time Data Engineering

• Design, develop, and maintain real-time data pipelines using Apache Flink and Apache Kafka.
• Develop production-grade streaming applications for high-volume and low-latency workloads.
• Implement data transformation, filtering, enrichment, aggregation, and event processing.
• Build reliable event-processing pipelines with appropriate error handling and recovery mechanisms.
• Consume and publish events across Kafka topics.
• Implement partitioning, consumer groups, offsets, and appropriate delivery mechanisms.
• Troubleshoot streaming pipeline failures, latency, throughput, and performance issues.

2. Apache Flink

• Develop and maintain production-grade Apache Flink jobs.
• Implement stream transformations, filtering, mapping, aggregations, joins, and windows.
• Work with event-time processing, watermarks, and state management.
• Implement Flink checkpointing, savepoints, and recovery mechanisms.
• Optimize Flink jobs for performance, scalability, and resource utilization.
• Monitor latency, throughput, failures, backpressure, and resource consumption.
• Troubleshoot state, checkpointing, backpressure, and processing issues.

3. Apache Kafka

• Develop Kafka-based ingestion and streaming pipelines.
• Create and manage Kafka topics and event streams.
• Work with partitions, offsets, consumer groups, replication, and retention.
• Develop reliable Kafka producer and consumer applications.
• Handle message ordering, retries, duplicate events, and replay scenarios.
• Monitor Kafka performance and troubleshoot consumer lag and throughput issues.
• Work with Kafka schemas and serialization formats.

4. CDC & Debezium

• Build CDC-based ingestion pipelines using Debezium.
• Configure and maintain Debezium connectors.
• Capture source-system inserts, updates, and deletes.
• Publish CDC events into Kafka.
• Handle initial snapshots and incremental CDC processing.
• Manage schema evolution and source-system changes.
• Implement data reconciliation and consistency checks.
• Troubleshoot CDC failures and source-to-target data issues.

5. Data Orchestration

• Develop and maintain data workflows using Apache Airflow or equivalent orchestration frameworks.
• Build reusable DAGs for:
• Data ingestion
• CDC workflows
• Data validation
• Flink job execution
• Data transformation
• ClickHouse loading
• Downstream integrations
• Implement workflow dependencies, scheduling, retries, backfills, SLAs, and alerting.
• Integrate Airflow with Kafka, Flink, Debezium, ClickHouse, APIs, and cloud services.
• Monitor workflow execution and troubleshoot failures.
• Develop reusable operators, sensors, and workflow components where required.
• Use event-driven triggers for real-time workflows where appropriate.

6. ClickHouse & Analytical Data

• Integrate streaming data pipelines with ClickHouse.
• Design efficient analytical data models.
• Develop and optimize SQL queries.
• Implement appropriate partitioning, sorting, indexing, and retention strategies.
• Optimize data ingestion and query performance.
• Support analytical use cases, dashboards, and reporting requirements.

7. Data Quality & Reliability

• Implement data validation and quality checks throughout the data pipeline.
• Build reconciliation mechanisms between source and target systems.
• Monitor data freshness, completeness, accuracy, and consistency.
• Implement error handling, retry, replay, and recovery mechanisms.
• Establish logging and observability for critical pipelines.
• Support incident investigation and root-cause analysis.

8. Integration & APIs

• Integrate streaming and analytical data with APIs, dashboards, endpoints, and downstream applications.
• Develop data interfaces and integration components.
• Work with application teams to define data contracts and integration requirements.
• Support future integrations and additional data consumers.

9. Engineering Practices

• Follow modern software engineering practices including:
• Git and version control
• Code reviews
• Unit and integration testing
• CI/CD
• Logging and monitoring
• Documentation
• Develop reusable, scalable, and maintainable data engineering components.
• Participate in technical design discussions and architecture reviews.
• Mentor Data Engineers and contribute to engineering standards.

Required Skills & Experience

• 6–8 years of experience in Data Engineering.
• Strong hands-on experience with Apache Flink – Mandatory/Core Requirement.
• Strong hands-on experience with Apache Kafka.
• Hands-on experience with Debezium and Change Data Capture (CDC).
• Strong programming experience in Java or Scala.
• Good experience with Python is an advantage.
• Strong SQL skills.
• Experience with analytical databases; ClickHouse is highly preferred.
• Hands-on experience with Apache Airflow or another data orchestration framework.
• Strong understanding of distributed systems and real-time data processing.
• Experience developing and supporting production-grade streaming pipelines.
• Strong understanding of Kafka concepts including:
• Topics
• Partitions
• Offsets
• Consumer Groups
• Replication
• Retention
• Experience with data transformation, enrichment, filtering, aggregation, and event processing.
• Strong troubleshooting and problem-solving skills for performance and reliability issues.
• Familiarity with cloud platforms and containerized environments.

Preferred Skills

• Advanced Apache Flink experience, including:
• State Management
• Checkpoints
• Savepoints
• Watermarks
• Event Time
• Windows
• Backpressure
• State Backends
• Experience with Apache Airflow, Dagster, Prefect, or Apache NiFi.
• Experience with Kafka Schema Registry.
• Experience with Avro, Protobuf, or JSON.
• Experience with Kubernetes.
• Experience with AWS, Azure, or GCP.
• Experience with CI/CD pipelines.
• Experience with Docker and containerized applications.
• Experience with Terraform or other Infrastructure as Code tools.
• Experience with data observability and monitoring tools.
• Experience building high-volume, low-latency real-time data platforms.
• Experience with REST APIs and system integrations.

Key Technical Stack

Apache Flink | Apache Kafka | Debezium | CDC | ClickHouse | Apache Airflow | Java/Scala | Python | SQL | Kubernetes | Docker | Cloud | CI/CD | REST APIs

Apply Now

Interested candidates can share their updated CV at [HIDDEN TEXT] or WhatsApp it to 8827565832.

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