Lead Data Engineer – Streaming Data

Navikenz; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Navikenz in Bengaluru seeks an experienced Lead Data Engineer to build scalable data pipelines and infrastructure for a next-generation data platform. The role collaborates with AI/ML teams and DevOps to enable analytics, forecasting, and anomaly detection.

You will design real-time and batch pipelines, manage data lakes/warehouses, and ensure data quality and observability across hybrid environments. Strong emphasis on IaC and automation.

Qualifications

• 6+ years in data engineering with real-time or streaming pipelines.
• Strong programming and SQL skills.
• Experience with Kafka, Spark, or Flink for real-time processing.
• Familiar with data lakes/warehouses and cloud platforms.

Responsibilities

• Design and implement real-time (Kafka, Spark/Flink) and batch (Airflow, Spark) pipelines for high-throughput data ingestion, processing, and transformation.
• Develop data models and manage data lakes and warehouses (Delta Lake, Iceberg, etc) to support analytical and ML workloads.
• Integrate data from diverse sources: IoT sensors, databases (SQL/NoSQL), REST APIs, and flat files.
• Ensure pipeline scalability, observability, and data quality through monitoring, alerting, validation, and lineage tracking.
• Collaborate with AI/ML teams to provision clean and ML-ready datasets for training and inference.
• Deploy, optimize, and manage pipelines and data infrastructure across on-premise and hybrid environments.
• Participate in architectural decisions to ensure resilient, cost-effective, and secure data flows.
• Contribute to infrastructure-as-code and automation for data deployment using Terraform, Ansible, or similar tools.

Skills

Real-time pipelines

Java

SQL

Data modeling

Collaboration with AI/ML teams

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Apache Kafka

Apache Spark

Apache Flink

Airflow

dbt

Terraform

Ansible

Job description

We are seeking an experienced Lead Data Engineer to build and maintain scalable, high-performance data pipelines and infrastructure for our next-generation data platform. The platform ingests and processes real-time and historical data from diverse industrial sources such as airport systems, sensors, cameras, and APIs. You will work closely with AI/ML engineers, data scientists, and DevOps to enable reliable analytics, forecasting, and anomaly detection use cases.

Key Responsibilities

• Design and implement real-time (Kafka, Spark/Flink) and batch (Airflow, Spark) pipelines for high-throughput data ingestion, processing, and transformation.
• Develop data models and manage data lakes and warehouses (Delta Lake, Iceberg, etc) to support both analytical and ML workloads.
• Integrate data from diverse sources: IoT sensors, databases (SQL/NoSQL), REST APIs, and flat files.
• Ensure pipeline scalability, observability, and data quality through monitoring, alerting, validation, and lineage tracking.
• Collaborate with AI/ML teams to provision clean and ML-ready datasets for training and inference.
• Deploy, optimize, and manage pipelines and data infrastructure across on-premise and hybrid environments.
• Participate in architectural decisions to ensure resilient, cost-effective, and secure data flows.
• Contribute to infrastructure-as-code and automation for data deployment using Terraform, Ansible, or similar tools.

Qualifications & Required Skills

• Bachelor’s or Master’s in Computer Science, Engineering, or related field.
• 6+ years in data engineering roles, with at least 2 years handling real-time or streaming pipelines.
• Strong programming skills in Java and SQL.
• Experience with Apache Kafka, Apache Spark, or Apache Flink for real-time and batch processing.
• Hands-on with Airflow, dbt, or other orchestration tools.
• Familiarity with data modeling (OLAP/OLTP), schema evolution, and format handling (Parquet, Avro, ORC).
• Experience with hybrid/on-prem and cloud platforms (AWS/GCP/Azure) deployments.
• Proficient in working with data lakes/warehouses like Snowflake, BigQuery, Redshift, or Delta Lake.
• Knowledge of DevOps practices, Docker/Kubernetes, Terraform or Ansible.
• Exposure to data observability, data cataloging, and quality tools (e.g., Great Expectations, OpenMetadata).