Job Description :

Role Overview :

As a Senior Data Engineer, you will play a key role in designing and implementing robust data engineering solutions and models that drive our data and analytics initiatives. Reporting to the Data Engineering and Platforms Manager and under guidance of the Lead Data Modeller/Engineer, you will contribute to the development and maintenance of our data infrastructure, ensuring data quality, governance, and optimal performance.

This role requires experience in data modeling and data solutions development, proficiency in technologies like Azure Synapse, Snowflake, and DataBricks, and a strong commitment to learning, adapting, integrity and growing towards leadership roles.

Stakeholders :

Master Data Project Team, IT and Operations teams, Enterprise architect, Data architects, business data owners, business data stewards, Data and analytics group, data and digital teams under functions like SCM and Commercial, BI engineers team.

Key Responsibilities :

Data Modeling and Architecture :

- Develop and maintain conceptual, logical, and physical data models to support business requirements.

- Collaborate with data architects to ensure alignment with overall data architecture and design principles.

Data Engineering :

- Design and implement scalable data pipelines for efficient extraction, transformation, and loading (ETL) of data.

- Must have experience in Azure DevOps release processes, follow coding standards, perform code reviews, and be capable of leading small to medium data engineering projects

- Strong experience in Azure services such as Synapse, ADF, Logic Apps, Azure Databricks, ADLS, VNet/Subnet, and network policies.

- The candidate should have good hands-on skills in data engineering, data modeling, and implementing the Medallion framework.

- Should be strong in Data warehousing concepts, Data Modeling, Solution Design, ETL techniques

- Utilize technologies such as ADF, Synapse, Snowflake, and DataBricks to optimize data processing and analytics.

Data Quality :

- Implement and manage data quality standards and controls for ensuring data pipelines and data assets are maintained with data quality standards such as data quality registers, reconciliation, referential integrity, code translations, correctness, and completeness of data.

Data Governance :

- Implement and enforce data governance principles, ensuring data quality, security, and compliance with regulations.

- Collaborate with the Data Governance and Security personnel to maintain data governance policies and procedures.

Mentorship and Leadership :

- Provide mentorship and guidance to junior data engineers and modelers.

- Lead by example in adopting best practices, coding standards, and data engineering principles.

- Collaborate with cross-functional teams to foster a culture of learning and innovation.

Collaboration and Communication :

- Work closely with data scientists, business analysts, and other stakeholders to understand data requirements and deliver solutions.

- Communicate complex technical concepts to non-technical stakeholders effectively.

Continuous Improvement :

- Stay abreast of emerging trends and technologies in data engineering and data modeling.

- Identify opportunities for process improvement and optimization within the data engineering function.

Qualifications :

Education : Bachelor's degree in Computer Science, Information Systems, or a related field. Advanced degree or relevant certification is a plus.

Experience : Minimum of 3-5 years of experience in data engineering and modeling roles, with a focus on complex data projects. Proven experience in data modeling tools and methodologies.

Technical Skills :

- Experience in implementing data and analytics solutions at scale on cloud technologies (preferably Azure)

- Proficiency in performant data modeling (normalized, star schemas, etc) with tools like Erwin/Safyr and data governance solutions like Purview.

- Hands-on experience with Synapse, Snowflake, DataBricks and business intelligence tools (e.g., Tableau, Power BI).

- Strong technical skills, including knowledge of data warehousing, cloud-based data platforms, and database management systems. (Snowflake, MS Synapse, Data Bricks, Azure Analytics Service, Cosmos DB, IoT Hub, Azure ML, MS Open AI etc.)

- Strong SQL skills and expertise in scripting languages (e.g., Python, Scala).

- Hands on experience with data cataloging and quality tools like Purview, Collebra, SAP Data services etc.

- Proficiency in ITIL service management framework including use of service management tools like JIRA and Confluence

Problem solving : Sound logical thinking and problem solving capabilities.

Communication :

- Excellent communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.

- Fluency in English

Collaboration : Strong collaborative skills, with the ability to work effectively in cross-functional teams.

Good to Have :

- In-depth knowledge of data privacy and protection regulations.

- Experience with big data technologies (e.g., Hadoop, Spark).

- Familiarity with machine learning and data science concepts.

- Any kind of business domain experience or specialization in areas like sales, supply chain, finance.

- Experience with utilizing data from SAP, APO, Salesforce.

- Training, experience, and certificates in Agile deliveries.

- Fluency in Japanese

KPIs to build and monitor :

- Data Pipeline Efficiency : Efficiency of data pipelines in terms of data processing time and resource utilization.

- Data Modeling Effectiveness : Impact and effectiveness of data models in meeting business requirements. (Feed-back and Survey Based)

- Data Quality Improvement : Improvements in data quality metrics over time. (Percentage)

- Mentorship Impact : Evaluation of the progress and growth of junior team members under mentorship. (Part of year end evaluation)

- Project Delivery Time : Measure the time taken to deliver complex data projects from initiation to completion.

- Data Model Accuracy : The percentage of data model changes that result in improved data accuracy.

- Team Productivity : Percentages and feedback on for project completion, timely delivery, number of defects after delivery, and adherence to best practices.

- Satisfaction : Qualitative feedback from team members, business counterparts, and data stewards to gauge their satisfaction with the reporting / dashboarding deliveries and leadership (score 0-5)