Skip to content
CV Rocket
Get started
How it worksThe enginePricingBlogContactSign in

Open nowPosted 23 days ago

Lead Data Engineer - Kafka

Relanto40 open roles

Where
Bengaluru
Get the CV for this job

From $25 per CV, paid once. No subscription.

Your applicationOpen nowLead Data Engineer - KafkaRelanto · Bengaluru

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Relanto's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 23 days ago

The posting

Role: Lead Data Engineer

Experience: 6–8 years

Location: Bengaluru / Hybrid

Employment: Full-time

Role Summary

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

The Lead Data Engineer will be responsible for designing, developing, and maintaining highperformance data pipelines using Apache Flink, Apache Kafka, Debezium, CDC, ClickHouse, and Apache Airflow.

This is a hands-on engineering role requiring strong practical experience in building productiongrade streaming applications, developing data pipelines, troubleshooting distributed systems, and optimizing data processing workloads.

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

Key Responsibilities

  • 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 appropriate partitioning, consumer groups, offsets, and delivery mechanisms.
  • Troubleshoot streaming pipeline failures and performance issues.

Apache Flink

  • Develop and maintain Apache Flink jobs forreal-time data processing.
  • Implement:Stream transformations Filtering Mapping Aggregations Joins Windows Event-time processing Watermarks State management
  • Implement Flink checkpointing and recovery mechanisms.
  • Optimize Flink jobs for performance, scalability, and resource utilization.
  • Monitor Flink jobs for latency, throughput, failures, and resource consumption.
  • Troubleshoot state, checkpointing, backpressure, and processing issues.

Kafka

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

CDC & Debezium

  • BuildCDC-based ingestion pipelines using Debezium.
  • Configure and maintain Debeziumconnectors.
  • Capture source-system inserts, updates,and deletes.
  • Publish CDC events into Kafka.
  • Handle initial snapshots and incremental CDC processing.
  • Manage schemaevolution and changesin source systems.
  • Implement data reconciliation and consistency checks.
  • Troubleshoot CDC failures and source-to-target data issues.

Data Orchestration

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

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 downstream analytical use cases,dashboards, and reporting requirements.

Data Quality& Reliability

  • Implement data validation and quality checksthroughout the pipeline.
  • Buildreconciliation mechanisms betweensource and target systems.
  • Monitor data freshness, completeness, accuracy, and consistency.
  • Implement error handling, retry,replay, and recoverymechanisms.
  • Establish loggingand observability for critical pipelines.
  • Support incident investigation and root-cause analysis.

Integration & APIs

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

Engineering Practices

  • Follow modernsoftware engineering practices around:Git Code reviews Unit testing Integration testing CI/CD Logging Monitoring Documentation
  • Develop reusable and maintainable data engineering components.
  • Participate in technical designdiscussions and architecture reviews.
  • Mentor other Data Engineers and contribute to engineering standards.

Required Skills& Experience

  • 6–8 years ofexperience in Data Engineering.
  • Strong hands-onexperience with Apache Flink — mandatory/core requirement.
  • Strong hands-on experience with Apache Kafka.
  • Hands-on experience with Debeziumand CDC.
  • Strong programming experience in Java or Scala.
  • Python experience is an advantage.
  • Strong SQL skills.
  • Experience with analytical databases; ClickHouse is highly preferred.
  • Hands-on experience with Apache Airflowor another data orchestration framework.
  • Strong understanding of distributed systemsand real-time data processing.
  • Experience developing and supporting production-grade streaming pipelines.
  • Strong understanding of Kafka:Topics Partitions Offsets Consumer groups Replication Retention
  • Experience with data transformation, enrichment, filtering, aggregation, and event processing.
  • Experience troubleshooting performance and reliability issues.
  • Familiarity with cloud platforms and containerized environments.

Preferred Skills

  • Advanced experience with Apache Flink, 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 / 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 buildinghigh-volume, low-latency real-time data platforms.
  • Experience with REST APIs and systemintegrations.
From $25, paid onceGet the CV for this job

What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against Relanto's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on Relanto's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    Relanto's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
Get the CV for this job

From $25 per CV, paid once. No subscription.

Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

Get the CV for this job

From $25 per CV, paid once. No subscription.

The numbers game

More applications. More interviews.

Every application goes out with its own CV, written for that posting and paid once. Send enough of them and the law of large numbers finds you the job.

Get the CV for this job
By hand5–10
With CV Rocket100
applications a day

Nearby

Live postings like this one

Same employer first, then the same role elsewhere.

Before you press

Straight answers

Get the CV for this job

From $25 per CV, paid once. No subscription.

What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

Yes, on the employer's own form, the hardest ones included. Where a question needs you, you answer it right there and AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

Is it a subscription?

No. You pay once per CV, from $25. Every application goes out with its own CV, written for that posting.

One job. One CV.
Paid once.

Pick the posting you want. We write for it, apply for you and catch the reply.

Get the CV for this job

From $25 per CV, paid once. No subscription.