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.