Staff Data Engineer

JOB DUTIES: Translate business requirements into data requirements, data warehouse design and sustaining data management strategies on enterprise data platforms like Snowflake. Work with project leads, stakeholders, and business SMEs to define technical specifications to develop data modeling requirements and maintain data infrastructure to provide business users with the tools and data needed. Solution architect, design and develop large scale and optimized analytics solutions. Requirements gathering and analysis, development planning and coordination in collaboration with stakeholder teams. Understand data architecture & solution design, design & develop dimensional models in an enterprise data warehouse environment. Development and automation of enterprise data transformation pipelines, ELT/ ETL processes. Work with cross functional teams and process owners on the development of test cases and scripts, test models and solutions to verify that requirements are met and ensuring high levels of data quality. Develop and apply quality assurance best practices. Design and apply data engineering best practices for an enterprise data warehouse. Analyze data and data behaviors to support business user queries. Excellent understanding of impact due to changes in data platforms, data models and data behaviors. Excellent problem-solving skills and ability to troubleshoot complex data engineering issues. Benchmark application operational performance periodically, track (metrics) and fix issues. Understand and comply with data governance and compliance practices as defined for risk management. This includes data encryption practices, RBAC and security policies. Promote and apply metadata management best practices supporting enterprise data catalogs. Support change and release management processes. Support incident and response management including problem solving and root cause analysis, documentation. Support automation and on-call processes (Tier 1 / Tier 2). Specific Skills Or Other Requirements: Requires 8 years of experience with: Primary Experience Data Warehouses and Data Transformations Data Platforms: Required: Snowflake cloud data platform Preferred: Denodo data virtualization, SAP HANA analytics Data Engineering: Snowflake: 5 years of required expertise with Snowflake SnowSQL, Snowpipe (integrated with AWS S3), Streams and Tasks, Stored Procedures, Merge statements, Functions, RBAC, Security Policies, Compute and Storage usage optimization techniques, Performance optimization techniques. Dbt Cloud: 3 years of expertise with dbt cloud platform, very good understanding of data models (views, data materializations, incremental data loads, snapshots), cross functional references, DAGs and its impact, job scheduling, audit and monitoring, working with code repositories, deployments. SnapLogic: Data integrations with SnapLogic integration platform is a plus. Certifications: Snowflake and dbt Cloud data engineering certifications is a plus. Data Orchestration: Required: Control-M, Apache Airflow Cloud Storage Platforms: Required: Amazon Web Services Preferred: Microsoft Azure Programming/ Scripting: Snowflake: SQL Scripting (Snowpipes, Tasks, Streams, Merge Statements, Stored Procedures, Functions, Security Policies DDM, Row-Access), SQL Scripting, Python Code Management: Required: Excellent understanding of working with code repositories like GitHub, GitLab ,code version management, branching and merging patterns in a central repository managing cross functional code deployments. Data Operations: Excellent understanding of Data Ops practices for data management. Solution Design: Good understanding of end-to-end solution architecture and design practices, ability to document solutions (maintain diagrams) Stakeholder Engagement: Ability to take the lead and drive project activities collaborating with Analytics stakeholders and ensure the requirement is completed. Data Warehousing: Excellent on fundamental concepts of dimensional modeling, experience working on data warehouse solutions, requirement gathering, design & build, data analysis, data quality. data validations, developing data transformations using ELT/ ETL patterns. Source Systems: Required : Knowledge and experience maintaining dimensional data model built on data from source systems like SAP ERP (On premises, Cloud), Salesforce CRM, Workday, Planisware, Team Center PLM. Other source systems are a plus. Data As-A-Product: Preferred knowledge and experience working with data treated as data products. Illumina is following a hybrid data mesh architecture which promotes data as a product for data lifecycle management. Governance: Good understanding of working with companies having regulated systems and processes for data. Adherence to data protection practices using tagging, security policies and data security (object-level, column-level, row-level). Promoting and applying best practices for data catalogs, following data classification practices and metadata management for data products within your scope. Operating Systems: Windows, Linux Education & Experience: Bachelors degree equivalent in Computer Science/Engineering or equivalent degree.

Location: bangalore, IN

Posted Date: 11/22/2024