Data Platform Engineer
by Salt in Information Technology (IT)
We are seeking a skilled Data Platform Engineer to build and scale reliable data infrastructure and pipelines, enabling analytics, product, and business teams through high-quality, performant, and accessible data across the organization. The role covers building and maintaining ETL/ELT pipelines for structured and unstructured data; designing, developing and optimizing batch and streaming pipelines using tools such as Kafka, Spark, or Flink; ensuring data integrity, availability and performance across data systems; and designing and optimizing data models for analytics and product use cases. The role also requires writing clean, maintainable, production-grade code, preferably Python, Java, Go, or Ruby, to support data workflows, internal data services, integrations and automation, as well as building internal tooling to improve data reliability and developer productivity. The Data Platform Engineer will partner with product, analytics and engineering teams to define data needs and infrastructure requirements, contribute to cross-functional system design discussions and architectural reviews, and ensure delivery of reliable infrastructure and data solutions that meet SLAs. Quality and best-practice responsibilities include implementing monitoring, alerting and logging for data pipelines; writing automated tests for data transformations and pipeline logic; and driving best practices for scalability, cost efficiency and data governance. Requirements include 4–6 years of experience in data platform or data engineering roles, strong proficiency in SQL, experience with relational and NoSQL databases, hands-on experience with Airflow, DBT, Spark, or Flink, experience with streaming or batch data processing systems, proficiency in at least one programming language including Python, Go, or Java, and familiarity with cloud platforms including AWS, GCP, or Azure. Nice-to-have experience includes real-time streaming using Kafka, Kinesis, or Pub/Sub; exposure to data warehouses such as Snowflake, BigQuery, or Redshift; experience operating data platforms in high-scale or fintech domains; and understanding of data security, privacy, and compliance.