3+ years of industrial experience in Big Data Engineering
Strong proficiency in SQL and at least one programming language: Python, Scala, or Java
Hands-on experience with AWS and Snowflake
Strong Experience in Data Build Tool (DBT)
Experience with cloud storages: AWS S3, Azure Storage, Google Cloud Storage
Practical skills with Hadoop-like environments (EMR, HDInsight, Dataproc)
Experience with Big Data frameworks: Apache Spark, Databricks, Apache Flink, Apache Beam
Knowledge of orchestration tools: Airflow, NiFi, AWS Glue, Azure Data Factory, Cloud Composer
Experience with streaming platforms: Kafka, Kinesis, SQS, Event Hub, Pub/Sub, IoT Hub
Familiarity with data warehousing and analytics: Snowflake, Redshift, Athena, Synapse
Experience working with containerization & serverless technologies (Docker, Lambda, Fargate, Functions, Cloud Run)
Understanding of data governance, data quality, and data lifecycle processes
Strong communication and collaboration skills
English at Upper?Intermediate level
Bachelor’s degree in Computer Science or similar (preferred)
AWS or Snowflake certifications (preferred)
Design and optimize distributed computing architectures for scalable Big Data processing
Build and enhance batch and streaming data pipelines
Implement advanced data processing: real?time analytics, ML-driven models, predictive insights
Develop reusable, parameterized ETL/ELT pipelines for DWH and lakehouse solutions
Conduct end?to?end data flow validation and performance optimization
Implement best practices for data quality, lineage, and governance
Configure and maintain CI/CD flows and release pipelines
Work with containerized and multi-container development environments
Troubleshoot complex data infrastructure and processing issues
Contribute to architectural decisions and technology selection
Ensure secure customer data management throughout the entire project lifecycle
Follow data retention, deletion, and off-boarding procedures
Mentor junior engineers and support technical decision-making
