Data Engineer( Cloud native)

Job Description

Role Overview

We are modernising an enterprise data warehouse to a cloud‑native analytics platform on AWS and Snowflake. We are looking for a hands‑on Snowflake Data Engineer to build and support reliable ingestion and ELT pipelines, dimensional data models, and production‑ready data solutions.

The role will work closely with senior engineers, architects, QA, cloud/platform teams, and BI users through design, development, testing, deployment, and operational handover.

Key Responsibilities

• Design, develop, and maintain data pipelines and data warehouse layers in Snowflake, including RAW, ODS, and DW schemas, tables, and views.
• Implement data ingestion and orchestration using Snowflake features such as Stages, Storage Integrations, Snowpipe, Tasks, Streams, and Stored Procedures, or equivalent approved patterns.
• Develop and maintain dimensional data models, including fact and dimension tables, transformations, aggregates, and lookup structures.
• Write and optimise SQL for data transformation, reconciliation, troubleshooting, and performance improvement.
• Implement data quality checks, validation, reconciliation, logging, and error‑handling controls.
• Support rerun, recovery, and replay activities for failed or incomplete data processing.
• Work with AWS services such as S3 and IAM as part of secure cloud data integration.
• Support SIT, UAT, deployment, documentation, and operational handover activities.
• Collaborate with senior engineers and architects, follow agreed engineering standards, and contribute to code reviews and continuous improvement.

Qualifications

Requirements (Must-Have)

• 3+ years of experience in data engineering, data warehousing, or database development.
• Hands‑on experience with Snowflake, or practical experience with another cloud data warehouse and the ability to ramp up quickly on Snowflake.
• Strong SQL skills, including joins, window functions, data transformations, and query troubleshooting.
• Good understanding of dimensional modelling, including fact and dimension table design.
• Experience developing or supporting ETL/ELT pipelines and production data workflows.
• Basic understanding of data quality, reconciliation, logging, retry, and operational support practices.
• Ability to work effectively in a delivery team, communicate clearly, and take ownership of assigned datasets or pipeline components.

Plus Points (Advantage)

• Hands‑on experience with Snowpipe, Tasks, Streams, Stored Procedures, Stages, or Storage Integrations.
• Oracle Data Warehouse or legacy data platform migration experience.
• Experience handling semi‑structured data such as XML, JSON, or Snowflake VARIANT.
• AWS experience, particularly S3, IAM, encryption, and cloud integration patterns.
• Experience with Git‑based development, CI/CD, or automated deployment practices for data solutions.
• Familiarity with Tableau or other BI tools and governed dataset publishing.
• Relevant Snowflake, cloud, data engineering, AI, ML, or GenAI certification(s).

What We Value

• Strong engineering fundamentals and willingness to learn.
• Practical problem‑solving and attention to data correctness.
• Clean implementation, documentation discipline, and reliable delivery.
• Ability to work in a fast‑paced project environment and collaborate with different technical teams.
• Ownership of assigned tasks and openness to technical guidance and feedback.
• A good overall match is more important than meeting every listed technology requirement.
• Ability to join immediately or within 1–2 months is preferred.

Role Expectations

• Able to independently deliver assigned pipelines, datasets, or data warehouse objects with guidance from senior team members.
• Able to investigate data issues, explain findings, and implement appropriate fixes.
• Comfortable supporting testing, deployment, and operational readiness activities.
• Able to learn Snowflake features and project‑specific patterns quickly.