Data Engineer
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Key skills for this role
About the Role
Saal.ai is seeking a Data Engineer to design, develop, and maintain scalable data platforms and pipelines supporting analytics, AI, and data-driven products. The role requires hands-on experience with modern data engineering technologies, cloud-native platforms, and distributed systems.
Key Skills for This Role
Responsibilities
- Define, design, and develop data ingestion frameworks for large scale data ingestion, storage, and management across structured, semi structured, unstructured and streaming data sources
- Design, develop, and maintain scalable batch and streaming data pipelines
- Design and implement workflows and pipelines using tools such as Apache NiFi, Kafka, Spark, Flink, and Airflow
- Design and implement data models supporting analytical and operational workloads
- Collaborate with internal product teams and third party service providers on system integrations and platform enhancements
- Deploy and support data workloads in cloud and containerized environments
- Monitor, troubleshoot, and optimize data platform performance and reliability
- Ensure solutions are scalable, observable, secure, and operationally manageable
Requirements
- Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Data Engineering, or related discipline
- 5 8 years of professional experience as a Data Engineer, Big Data Engineer, or Data Platform Engineer
- Strong experience designing, building, and operating data pipelines in production environments
- Experience with modern Data Lake or Lakehouse architectures (Delta Lake, Iceberg, or equivalent)
- Proficient in batch and real time data processing with data formats such as Avro, Parquet, ORC
- Experience with Azure Databricks, Azure Data Lake Storage, Azure Event Hubs, and Azure Synapse Analytics
- Strong scripting or programming skills
- Experience working in Agile software development environments
Full Job Posting
Job Purpose
- As a Data Engineer, you will be responsible for designing, developing, and maintaining scalable data platforms and data pipelines that support analytics, AI, operational reporting, and data driven products.
Key Responsibilities
- Define, design, and develop data ingestion frameworks for large scale data ingestion, storage, and management across structured, semi structured, unstructured and streaming data sources.
- Design, develop, and maintain scalable batch and streaming data pipelines.
- Design and implement workflows and pipelines using tools such as Apache NiFi, Kafka, Spark, Flink, and Airflow.
- Design and implement data models supporting analytical and operational workloads.
- Collaborate with internal product teams and third party service providers on system integrations and platform enhancements.
- Participate in sprint planning, development, and delivery activities.
- Deploy and support data workloads in cloud and containerized environments.
- Monitor, troubleshoot, and optimize data platform performance and reliability.
- Ensure solutions are scalable, observable, secure, and operationally manageable.
Essential Skills & Experience
- Strong experience designing, building, and operating data pipelines in production environments.
- Experience working with modern Data Lake or Lakehouse architectures, including Delta Lake, Iceberg, or equivalent technologies.
- Solid understanding of distributed data architectures, data modelling, OLAP design, and change data capture (CDC), including tools such as Debezium.
- Experience with large scale storage and messaging platforms, including MinIO/S3 and Kafka.
- Experience integrating with enterprise systems through REST APIs, messaging platforms, and event driven architectures.
- Proficient in batch and real time data processing, with familiarity in data formats such as Avro, Parquet, and ORC.
- Strong scripting or programming skills.
- Experience delivering enterprise scale analytics and reporting platforms.
- Familiarity with monitoring and observability tools such as Grafana, Prometheus, ELK/OpenSearch, Dynatrace, or equivalent.
- Experience working in Agile software development environments.
- Experience with Azure Databricks, Azure Data Lake Storage, Azure Event Hubs, and Azure Synapse Analytics.
- Understanding of data governance, data lineage, access controls, and data security best practices.
Education and Qualifications
- Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Data Engineering, or a related discipline.
- Relevant professional certifications are advantageous.
- 5–8 years of professional experience as a Data Engineer, Big Data Engineer, or Data Platform Engineer
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