Data Engineer

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 support

analytics, 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 data

using 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, and

ElasticSearch.

• 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.