Lead Data Architect

Job Role

Location

Actionable:

1. Work with cross functional teams to make the data usable for functional users and applications to enable delivery of business values to customers
2. Provide architectural leadership and vision for Bank's Next Gen Data Platform and Data Lakehouse
3. Develop and maintain architectural roadmap for data products and data services plus ensuring alignment with the business and enterprise architecture strategies and standards
4. Drive design and architecture for Data Transformation & aggregations, design and development of a roadmap, and implementation based upon current vs. future state in a cohesive architecture viewpoint
5. Review and understand business requirements and technical designs for physical data design, data pipelines (ETL) and other technical integrations
6. Build data pipelines for multiple storage solutions, including distributed platforms such as Databricks, Trino, Hadoop and MPP databases and cloud Data warehouse
7. Design and implement low latency analytical platform services leveraging open source and cloud technology
8. Design and implement data governance and MDM tools
9. Designing and building data capabilities to support cloud data strategy, including fully automated data Pipelines, data curation and consumption
10. Build, and validate migration of workloads from 3rd party databases and on prem data platforms to cloud
11. Drive internal proof of concept initiatives, when needed, quickly design and implement a prototype of a system or component with a proper architecture, and then hand over to (may lead) a small group of developers to implement it

Key Skills

1. Experienced technology leader with a minimum of 12+ years of software development experience including 10+ years of data application or data platform architecture experience with deep technology expertise
2. Deep knowledge and hands on experience in design and implementation of Big Data technologies (Apache Spark, Apache Airflow, Apache Flink, Streaming data, ADLS, S3, Object Data store, Trino, Databricks, Unity Catalog, Data governance tools) and familiarity with data architecture patterns (data warehouse, data lake, data lakehouse, data ingestion, curation and consumption) Expertise in metadata management, reference data management. Expertise in streaming and batching data processing and efficient and performing organization of data in devices set of data platforms
3. Experience building and deploying a range of data engineering pipelines into production, including using automation best practices for CI/CD
4. Experience with cloud providers such as Azure, AWS (preferably Azure) and their native services for ingestion, ELT/ ETL and consumption
5. Experience in setting up and managing Aws, Azurebased Data Capabilities, data flow automation for real time and batch data pipelines across multiple data sources (on prem & cloud).
6. Solid understanding of data modeling and data transformation techniques
7. General knowledge of Cloud solutions and technical architecture, as well as the impact of implementing them in an enterprise organization
8. Highly organized, self-motivated, pro-active, and able to plan
9. Experience managing users and vendors is a plus