Senior Data Engineer at Meili

Dublin, Dublin County, Ireland -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 27

Salary

50000.0

Posted On

03 Oct, 26

Experience

4 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Industry

Information Technology & Services

Description

Design, build and maintain the data pipelines that feed our warehouse, including ingestion from partner systems and third-party sources, each with its own quirks and reliability profile.
Own and extend our modelled analytics layer in Snowflake, turning raw source data into well-documented, well-tested tables that the business can query directly.
Build data quality and observability into the platform: tests, freshness checks, alerting and lineage, so we find out about a broken feed before a partner does.
Model the metrics the business runs on, so that a number means the same thing wherever it appears.
Partner directly with commercial, partnerships and product colleagues to understand what they actually need, and translate vague questions into durable data models rather than one-off queries.
Set engineering standards for the data team - version control, code review, CI, environments, documentation - and raise the bar for how we work.
Mentor, coach and train others, while acting as a technical sounding board across the team.

About you

Substantial building production data pipelines and warehouse models, with clear ownership of systems others depended on.
Strong, fluent SQL - window functions, incremental logic, query tuning - and solid experience with a cloud warehouse. Snowflake specifically is a real advantage.
Confident Python for data engineering: ingestion, transformation, testing.
Dimensional and event-based modelling instincts: you can look at a messy data source and design something sane on top of it.
Software engineering discipline applied to data - Git, code review, CI/CD and testing.
Experience with warehouse cost management and query optimisation at scale.
The communication skills to work with non-technical stakeholders, push back constructively, and create documentation that a colleague can pick up and build on.

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Responsibilities

Design, build and maintain the data pipelines that feed our warehouse, including ingestion from partner systems and third-party sources, each with its own quirks and reliability profile.
Own and extend our modelled analytics layer in Snowflake, turning raw source data into well-documented, well-tested tables that the business can query directly.
Build data quality and observability into the platform: tests, freshness checks, alerting and lineage, so we find out about a broken feed before a partner does.
Model the metrics the business runs on, so that a number means the same thing wherever it appears.
Partner directly with commercial, partnerships and product colleagues to understand what they actually need, and translate vague questions into durable data models rather than one-off queries.
Set engineering standards for the data team - version control, code review, CI, environments, documentation - and raise the bar for how we work.
Mentor, coach and train others, while acting as a technical sounding board across the team.

Substantial building production data pipelines and warehouse models, with clear ownership of systems others depended on.
Strong, fluent SQL - window functions, incremental logic, query tuning - and solid experience with a cloud warehouse. Snowflake specifically is a real advantage.
Confident Python for data engineering: ingestion, transformation, testing.
Dimensional and event-based modelling instincts: you can look at a messy data source and design something sane on top of it.
Software engineering discipline applied to data - Git, code review, CI/CD and testing.
Experience with warehouse cost management and query optimisation at scale.
The communication skills to work with non-technical stakeholders, push back constructively, and create documentation that a colleague can pick up and build on.