Role overview :- 2 - 6 years of experience in data engineering or analytics engineering roles.- Strong problem-solving and ability to work independently with guidance from senior engineers.- Good communication skills to translate business requirements into technical data solutions.- Ability to work in Agile environments and collaborate with global teams across time zones.- Hands-on experience with Azure Databricks and Apache Spark (PySpark or Scala) for large-scale data processing.- Robust SQL skills, including complex queries, window functions, CTEs, and query optimization for both transactional and analytical workloads.- Experience with SQL Server: data warehousing concepts, schema design, indexing, and optimization for analytics.- Familiarity with Azure data services: Data Lake Storage, Synapse Analytics, Data Factory, or Blob Storage.- Understanding of ETL/ELT concepts, data modeling, dimensional modeling (fact/dimension tables), and slowly changing dimensions.- Experience with batch and/or stream processing concepts; familiarity with event-driven architectures.- Knowledge of data quality frameworks, testing strategies,



and monitoring/alerting for data pipelines.- Basic understanding of relational and NoSQL databases (Cosmos DB exposure is beneficial).What would you do here :- The Data Engineering team builds and operates robust, scalable data platforms on Azure and Databricks, closely partnering with domain PODs, Risk, Finance, and Analytics teams across the globe. - You'll work on end-to-end data pipelines that ingest, transform, and serve data to multiple downstream consumers with strict quality and SLA requirements.- Build and maintain ETL/ELT pipelines on Azure Databricks using PySpark or Scala to ingest, transform, and deliver data products at scale.- Ingest data from SQL Server, Cosmos DB, event streams, APIs, and external sources into cloud data lakes and Databricks lakehouses.- Design and implement data quality checks, validation rules, and monitoring to ensure reliability and early detection of anomalies.- Optimize Spark jobs for performance and cost, including cluster sizing, partitioning strategies, and caching mechanisms.- Document data pipelines, lineage, and schemas; maintain data dictionaries and metadata catalogs for consumer discovery and understanding.- Collaborate with data analysts, scientists, and domain engineers to understand requirements and refine data products.- Contribute to infrastructure and tooling for data workflows (scheduling, orchestration, monitoring, alerting). (ref:hirist.tech)

📌 Data Engineer - ETL/PySpark (Bengaluru)
🏢 SWAI TECHNOLOGIES PRIVATE
📍 Bengaluru