We’re Hiring! - Data Engineer

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, Snow pipe, 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 optimize 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.

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.

Hands-on experience with Snow pipe, 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).

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.

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.