Data Engineer, Vapi

Job Title: Data Engineer Experience Required: Minimum 1–2 Years Location: Vapi, Gujarat Employment Type: Full-time About the Role We are seeking a skilled and motivated Data Engineer to join our growing technology team. The role involves building and maintaining scalable, reliable, and secure data infrastructure to supportanalytics, data-driven decision-making, and AI/ML pipelines. You'll work with diverse data types and modern data platforms to design efficient data pipelines and ensure smooth data flow across systems. Key Responsibilities• Design, develop, and maintain robust ETL/ELT pipelines for structured and unstructured datausing tools like Apache NiFi, Airflow, or Dagster.• Build streaming and event-driven data pipelines using Kafka, RabbitMQ, or similar systems. • Design and manage scalable data lakes (e.g., Apache Hudi, Iceberg, Delta Lake) over Amazon S3 or MinIO. • Implement and optimize distributed databases such as Cassandra, MongoDB, ClickHouse, andElasticSearch.• Ensure data quality, monitoring, and observability across all data pipeline components. • Work with query engines like Trino for federated data access. • Manage data versioning and reproducibility using tools like DVC. • Perform data migrations, query optimization, and system performance tuning.• Collaborate with analytics, product, and AI teams to provide clean and well-structured datasets. Must-Have Skills&Experience• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. • 1–2 years of experience as a Data Engineer or in a similar role. • Strong proficiency in Python and SQL. • Hands-on experience with ETL orchestration tools (Airflow, NiFi, Dagster).• Familiarity with data lakes, streaming platforms, and distributed databases. • Experience working with cloud/object storage (Amazon S3, MinIO). • Knowledge of data governance, security, and pipeline observability. Good-to-Have Skills • Experience with time-series databases (InfluxDB,TimescaleDB, QuestDB).• Familiarity with graph databases (Neo4j, OrientDB, or RavenDB). • Understanding of MLOps, feature stores, or data lifecycle automation. • Exposure to Elasticsearch for indexing and search use cases. • Experience in query performance tuning and data migration strategies.