Navikenz is seeking a highly skilled Lead Data Engineers to design, build, and optimize scalable, high-performance data platforms that power next-generation analytics and AI-driven solutions. In this role, you will develop robust data pipelines and infrastructure capable of ingesting, processing, and managing both real-time and historical data from a wide range of industrial sources, including airport systems, IoT sensors, cameras, enterprise applications, and external APIs.
You will collaborate closely with Data Scientists, AI/ML Engineers, Product Teams, and DevOps professionals to deliver reliable, secure, and scalable data solutions that enable advanced analytics, predictive forecasting, operational intelligence, and anomaly detection. This is an opportunity to work on complex, high-impact data engineering challenges in a fast-paced environment at the forefront of digital transformation.
Key Responsibilitie
- sDesign 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 Skil
- lsBachelor's or Master's in Computer Science, Engineering, or related fiel
- d.6+ years in data engineering roles, with at least 2 years handling real-time or streaming pipeline
- s.Strong programming skills in Python/Java and SQ
- L.Experience with Apache Kafka, Apache Spark, or Apache Flink for real-time and batch processin
- g.Hands-on with Airflow, dbt, or other orchestration tool
- s.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) deployment
- s.Proficient in working with data lakes/warehouses like Snowflake, BigQuery, Redshift, or Delta Lak
- e.Knowledge of DevOps practices, Docker/Kubernetes, Terraform or Ansibl
- e.Exposure to data observability, data cataloging, and quality tools (e.g., Great Expectations, OpenMetadata
).
