Data Architect, Common Data Products

Identifying data architecture improvement needs and opportunities and ensure that the improvements are incorporated into roadmaps, and to source systems or data consuming systems roadmaps, when required.

Supporting in incident handling process when the incident is related to data. Helping in identifying data flow and data quality flaws causing these incidents and preparing change proposals to permanently fix them.

To succeed in Data Architect role, following professional experience will play a key role:

Master’s degree in engineering, computer science, management information systems, or a related field

12-15+ years of professional hands-on experience in data architecture, data modeling, data engineering, analytical data processing, software engineering in enterprise working environment.

Subject matter experience in finance processes and genuine passion understand the data produced in the finance business processes. Previous working experience in enterprise systems, such as SAP ERP FI/CO, FSCM.

Experience in working with complex data enterprise landscapes (raw, semi structured, and unstructured data with different patterns batch, real-time, streaming) from multiple business processes and applications.

• Ability to write clear documentation and visualize data architecture design as data pipelines and data models (3NF, dimensional modeling) with clear information requirements. Ability to provide data architecture support on hands-on level to data engineering teams.

Ability to write clear documentation and visualize data architecture design as data pipelines and data models (3NF, dimensional modeling) with clear information requirements. Ability to provide data architecture support on hands-on level to data engineering teams.

• Proven coding skills with multiple languages (SQL, Python) with hands-on experience in industry standard software development life cycle methods, DevSecOps/DataOps practices spanning the full data product/data pipeline lifecycle.

Proven coding skills with multiple languages (SQL, Python) with hands-on experience in industry standard software development life cycle methods, DevSecOps/DataOps practices spanning the full data product/data pipeline lifecycle.

• Technical expertise in following tech stack: Databricks data engineering, multi-hop medallion architecture, Unity Catalog, Airflow, DBT, AWS Glue, AWS EMR, AWS Athena, Spark, Apache Flink, Apache Kafka, Terraform, Gitlab/Github. (Databricks and AWS certifications is an advantage).

Technical expertise in following tech stack: Databricks data engineering, multi-hop medallion architecture, Unity Catalog, Airflow, DBT, AWS Glue, AWS EMR, AWS Athena, Spark, Apache Flink, Apache Kafka, Terraform, Gitlab/Github. (Databricks and AWS certifications is an advantage).

Expertise in cybersecurity guidelines, data privacy, compliancy regulations and quality assurance practices, and cloud FinOps.

Experience in coaching and/or mentoring, proactive knowledge sharing and maturing DataOps practices.

Passion to utilize agile development methodologies and tools (Jira, Confluence, draw.io).

Ability to work collaborate and deliver in agile sprints. Ability to work in a global multi-cultural team across different countries and effectively collaborate within the teams. Ability to self-organize and be proactive, seek feedback, be courageous and resilient, and have excellent problem-solving skills.

• Proficiency in spoken and written English language, and strong facilitation and communication skills.