Data Warehouse Architect

Responsibilities

Creating the future data architecture/platform and generating data management artifacts, including data models, dictionaries, catalogs, and flows; ETL source-to-target mappings; and testing use cases. Enforcing data management best practices, standards, policies, and rules across different functional areas. Developing conceptual, logical, and physical data models based on business requirements to support various applications. Participating in data warehouse implementation, including data model design, ETL process coding, and data archiving and recovery. Evaluating and recommending emerging technologies for data management, storage for on-premise, cloud, and hybrid solutions. Designing and maintaining data warehouse ETL processes to solve complex data problems, including performance, scalability, security, and integration issues. Understand the client’s existing data management environments, ask the right questions, and be able to deep dive and identify the root cause of issues. Evaluate and plan DWH migrations to the cloud. Based on project requirements, collaborate with onshore and offshore teams to develop and deliver solutions. Guide teams to write better code. Review code and help in pushing the team in an optimized way of development. Experience working with large data, Data Ingestion, Data Import Process, Streaming Data. Ability to work in a fast-paced and agile development environment. Implementing data governance platforms and providing a systematic process and framework to access and share data across various business units within the company. Ability to communicate, engage and collaborate with multiple stakeholders to work on designing a data warehouse. Should be able to understand and write very complex SQL code.

Requirements

Hands-on experience with database technologies and data warehouses (SQL, PL/SQL, Oracle Exadata, Redshift, etc.) Hands-on experience as an ETL Architect well versed in ETL tool usage (Informatica) and its architecture, design, and development. Should have strong SQL Skills. Hands-on experience in using erwin and SqlDBM tools. Exposure to Unix scripting, Performance tuning, AutoSys, or any other orchestration tools will be an add-on. Prior experience in design and development of data pipelines and data warehouses using AWS tech stack – AWS Glue, Data pipelines, AWS Redshift, Kinesis, EMR will be helpful but not necessary. Should be an expert in Cloud Architecture, designing new solutions, driving team, and must have hands-on experience. Designing and implementing the data warehouse, operational data layers, and ETL/ELT processes Extensive hands-on experience in working on large-scale MPP enterprise data warehousing (EDW) projects. Should be very strong in Data Modelling and Database design. Knowledge of Python will be helpful.

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