Databricks Engineer

20 hours ago

India TTC Group Full time

Role: Databricks EngineerPosition: RemoteType: ContractDuration: 3 - 6 months (extendable) Role OverviewWe are seeking a Mid-Level Databricks Engineer with strong data engineering fundamentals and hands-on experience building scalable data pipelines on the Databricks platform. The ideal candidate will be comfortable working with distributed data processing, designing Delta Lake architectures, implementing CI/CD for data workloads, and optimizing Spark jobs for performance and reliability.Required SkillsStrong hands-on experience in building data pipeline using scripting language includes Python, PySpark and SQLStrong working experience with Databricks (Unity Catalog is plus) and Azure Data Factory (ADF)Hands-on working experience with Azure DevOps Repo and CI/CDStrong understanding of modern Data Architecture, Data Warehousing concepts, Data Modelling and ETL/ELT processes Understanding of Agile ways of workingAny experience working in DBT is a plusWorking with Retail Domain is a plusPlatform Engineering & OperationsConfigure and optimize Databricks clusters, including autoscaling, cluster sizing, and runtime selection.Manage databases, schemas, and Delta Lake tables, including vacuuming, Z-ordering, partitioning, and optimization.Work with catalogs, Unity Catalog (UC), and access controls for secure data governance.Monitor and troubleshoot performance issues across jobs, clusters, and workflows.CI/CD & DevOpsImplement CI/CD pipelines for Databricks workflows using GitHub Actions / Azure DevOps / GitLab.Work with Databricks Repos and Git workflows (branching, PRs, code reviews).Version and promote data pipelines across environments.Cloud & IntegrationIntegrate Databricks with cloud services such as:Azure: ADLS Gen2, Key Vault, ADF, Event Hub, SynapseAWS: S3, Glue, IAM, Lambda, Kinesis (depending on cloud environment)Work with data ingestion frameworks, messaging systems, and event-driven architectures.Quality, Testing & DocumentationImplement unit tests and integration tests using pytest, SQL tests, or dbx testing frameworks.Maintain pipeline documentation, data dictionaries, and technical design artifacts.Ensure high data quality, reliability, and observability.

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