JOB TITLE Senior Databricks Data Engineer Overview We are seeking aSenior Databricks Data Engineerto design build and operate a Data AI platform with a strong foundation in the Medallion Architecture raw bronze curated silver and mart gold layers This platform will orchestrate complex data workflows and scalable ELT pipelines to integrate data from enterprise systems such as PeopleSoft D2L and Salesforce delivering highquality governed data for machine learning AI BI and analytics at scale You will play asenior technical role in guiding engineering standards reusable patterns platform reliability and production readiness across the Data AI platform You will play a critical role in engineering the infrastructure and workflows that enable seamless data flow across the enterprise ensure operational excellence and provide the backbone for strategic decisionmaking predictive modeling and innovation Responsibilities 1 Data AI Platform Engineering DatabricksCentric Provide seniorlevel technical leadership in the design optimization and standardization of Databricks engineering patterns across the Data AI platform Design implement and optimize endtoend data pipelines on Databricks following the Medallion Architecture principles Build robust and scalable ETL ELT pipelines using Apache Spark and Delta Lake to transform raw bronze data into trusted curated silver and analyticsready gold data layers Operationalize Databricks Workflows for orchestration dependency management and pipeline automation Apply schema evolution and data versioning to support agile data development 2 Platform Integration Data Ingestion Lead the design of reusable ingestion frameworks and integration patterns that support scalable reliable and governed data onboarding across enterprise systems Connect and ingest data from enterprise systems such as PeopleSoft D2L and Salesforce using APIs JDBC or other integration frameworks Implement connectors and ingestion frameworks that accommodate structured semistructured and unstructured data Design standardized data ingestion processes with automated error handling retries and alerting 3 Data Quality Monitoring and Governance Establish seniorlevel data quality observability and governance practices to improve trust reliability lineage and operational transparency across the platform Develop data quality checks validation rules and anomaly detection mechanisms to ensure data integrity across all layers Integrate monitoring and observability tools e g Databricks metrics Grafana to track ETL performance latency and failures Implement Unity Catalog or equivalent tools for centralized metadata management data lineage and governance policy enforcement 4 Security Privacy and Compliance Provide senior technical guidance on secure data engineering practices accesscontrol patterns and compliance implementation across Databricks and related cloud environments Enforce data security best practices including rowlevel security encryption at rest in transit and finegrained access control via Unity Catalog Design and implement data masking tokenization and anonymization for compliance with privacy regulations e g GDPR FERPA Work with security teams to audit and certify compliance controls 5 AI MLReady Data Foundation Partner with data science and AI ML teams to shape reusable productionready data engineering patterns that support scalable model development deployment and monitoring Enable data scientists by delivering highquality featurerich data sets for model training and inference Support AIOps MLOps lifecycle workflows using MLflow for experiment tracking model registry and deployment within Databricks Collaborate with AI ML teams to create reusable feature stores and training pipelines 6 Cloud Data Architecture and Storage Contribute to seniorlevel cloud data architecture decisions related to data lake design storage optimization compute efficiency security and cost management Architect and manage data lakes on Azure Data Lake Storage ADLS or Amazon S3 and design ingestion pipelines to feed the bronze layer Build data marts and warehousing solutions using platforms like Databricks Optimize data storage and access patterns for performance and costefficiency 7 Documentation Enablement Mentor engineers and promote seniorlevel engineering standards through documentation code reviews reusable frameworks and knowledgesharing practices Maintain technical documentation architecture diagrams data dictionaries and runbooks for all pipelines and components Provide training and enablement sessions to internal stakeholders on the Databricks platform Medallion Architecture and data governance practices Conduct code reviews and promote reusable patterns and frameworks across teams 8 Reporting and Accountability Take ownership of complex data engineering deliverables production issues technical risks and crossteam dependencies requiring seniorlevel judgment and coordination Submit a weekly schedule of hours worked and progress reports outlining completed tasks upcoming plans and blockers Track deliverables against roadmap milestones and communicate risks or dependencies Required Qualifications Seniorlevel handson experience designing building optimizing and supporting productiongrade Databricks data engineering solutions in enterprise environments 5 years of handson experience with Databricks Delta Lake and Apache Spark for largescale data engineering Deep understanding of ELT pipeline development orchestration and monitoring in cloudnative environments Experience implementing Medallion Architecture Bronze Silver Gold and working with data versioning and schema enforcement in enterprise grade environments Solid proficiency in SQL Python or Scala for data transformations and workflow logic Proven experience integrating enterprise platforms e g PeopleSoft Salesforce D2L into centralized data platforms Familiarity with data governance lineage tracking and metadata management tools Preferred Qualifications Experience serving as a senior engineer technical lead or mentor on enterprise data platform or Databricks implementation initiatives Prior CLIENT or USM experience preferred Experience with Databricks Unity Catalog for metadata management and access control Experience deploying ML models at scale using MLFlow or similar MLOps tools Familiarity with cloud platforms like Azure or AWS including storage security and networking aspects Knowledge of data warehouse design and star snowflake schema modeling

📌 Senior Databricks Data Engineer (Hyderabad)
🏢 Artech Infosystems Private
📍 Hyderabad