Data Engineer

Key Responsibilities:

Data Engineering & Pipeline Development

• Develop and maintain scalable data pipelines for ingestion and transformation.
• Write advanced, optimised SQL queries (mandatory core skill).
• Use Python for transformation logic, automation, and data processing.
• Work across Snowflake, AWS, and GCP platforms.
• Support ingestion and harmonisation of multi-source ERP data.
• Optimise pipelines for performance, scalability, and cost efficiency.

Data Analysis & Dataset Development

• Analyse datasets to identify trends, anomalies, and inconsistencies.
• Build structured and business-ready datasets for reporting and analytics.
• Translate stakeholder requirements into reliable transformations.
• Ensure datasets are intuitive, governed, and reusable.
• Support downstream analytics and reporting use cases.

Data Modelling & Structured Design

• Assist in implementing logical and physical data models.
• Apply strong data modelling fundamentals.
• Build clean, reusable, analytics-ready datasets aligned with enterprise standards.
• Contribute to master data and finance reporting layers.
• Hands -on modelling exposure is expected.

Data Quality & Governance

• Perform validation, profiling, and quality checks using Ataccama (required experience).
• Work with Atlan or similar data catalog/governance tools (required).
• Support metadata documentation and lineage tracking.
• Identify and resolve inconsistencies across ERP sources.
• Contribute to enterprise standardisation and governance initiatives.

Business Collaboration

• Work closely with Finance and Corporate stakeholders.
• Understand KPIs, business definitions, and data expectations.
• Translate business logic into structured transformations.
• Ensure datasets are aligned to real-world business usage.

Engineering Discipline & Growth

• Follow best practices in SQL, Python, and pipeline design.
• Collaborate with Senior Engineers and Tech Leads.
• Adhere to CI/CD and DevOps practices.
• Continuously improve data reliability, documentation, and usability.
• Build capability toward Senior Data Engineer (P2) and Specialist (P3) roles.

Required Qualifications

• 4–7 years of experience in data engineering and/or data analysis.
• Strong hands-on SQL expertise
• Python programming experience.
• Practical understanding of data modelling fundamentals.
• Experience with Snowflake.
• Experience working on AWS and GCP.
• Experience with Ataccama.
• Experience with Atlan or similar data catalog/governance tools.
• Experience building and maintaining data pipelines and analytical datasets.
• Strong analytical and problem-solving mindset.
• Ability to work across both engineering and analytical tasks in a hands-on capacity.

Preferred Qualifications:

• ERP exposure (SAP ECC, JDE, Baan, etc.).
• Experience with master data or finance datasets.
• Understanding of data governance, lineage, and metadata concepts.
• Familiarity with CI/CD and DevOps practices.
  • Exposure to enterprise-scale data environments.