Data Engineer

About Halcer Halcer is a leading IT services, consulting, and staffing organization committed to delivering innovative technology solutions and top-tier technical talent to global enterprises.
Job Overview We are seeking an experienced Data Engineer (6+ Years of Experience) 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 teams 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 & Experience Requirements
Total Experience: 6+ years of total experience in core data engineering roles.
Streaming & Real-Time Experience: 2+ years of direct experience building and maintaining real-time or streaming pipelines.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Required Skills
Strong programming proficiency in Python or Java, alongside expert-level SQL.
Hands-on experience with Apache Kafka, Apache Spark, or Apache Flink for real-time and batch processing.
Proficiency with workflow orchestration tools like Airflow, dbt, or similar technologies.
Deep familiarity with data modeling (OLAP/OLTP), schema evolution, and file formats (Parquet, Avro, ORC).
Hands-on experience with hybrid/on-premise and cloud platform deployments (AWS, GCP, or Azure).
Proven track record working with data lakes and modern data warehouses (Snowflake, BigQuery, Redshift, or Delta Lake).
Strong working knowledge of DevOps practices, Docker, Kubernetes, and IaC tools like Terraform or Ansible.
Knowledge of data observability, data cataloging, and quality frameworks (e.g., Great Expectations, OpenMetadata).
Good-to-Have Skills
Experience with time-series databases (e.g., InfluxDB, TimescaleDB) and processing high-frequency sensor data.
Prior domain experience in aviation, manufacturing, or logistics.

Work arrangement
No