Data Engineer – Middle

Intermedia Cloud Communications

Data Engineer at Intermedia building and fixing data pipelines for cloud communications. Collaborating with teams across the globe while working with modern tech stack.

Posted 6/17/2026full-timeCoimbra • 🇵🇹 PortugalMid-LevelSeniorWebsite

ATS Keywords

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Hard Skills

SQLdbtdata engineeringdata analysisMSSQLSnowflakeOraclePostgreSQLincremental loadsstored procedures

Soft Skills

ownershipproblem-solvingattention to detaildocumentationself-motivationcommunicationcollaborationadaptabilitycritical thinkingtime management

Tools & Technologies

GitS3monitoring toolsdata pipelinesdata transformation toolsdata ingestion toolsdata consumption toolsversion control systemsCI/CD toolsdata validation tools

Industry Keywords

data pipelinesdata transformationdata ingestiondata consumptiondata validationdata monitoringdata architectureETL processesdata warehousingdata governance

Tech Stack

Tools & technologies

OraclePostgresSQL

About the role

Key responsibilities & impact

Build and fix pipelines — ingestion, transformation, consumption, all of it
Write dbt models that actually have tests and docs (yes, we check)
Chase down a data issue from the report all the way back to the source
Jump on an incident — MSSQL job died, Snowflake task hung, S3 file vanished — and fix it
Run a backfill, validate the results, confirm the fix actually worked
Help move data from MSSQL into Snowflake without losing anything along the way
Keep an eye on monitoring so users don’t find problems before you do
Leave things documented well enough that future-you will say thank you

Requirements

What you’ll need

You own your work — no hand-holding, no reminders, you close the loop
A degree in Computer Science, Data Analytics, Statistics, Mathematics, Economics, or something similar
3–5 years doing data engineering, data analysis, or something close
SQL that actually impresses people — joins, window functions, CTEs, and you know why that query is slow
You can read someone else’s stored procedure and figure out what went wrong
You have shipped something with a real database in production: MSSQL, Oracle, PostgreSQL, or similar
Layers make sense to you: raw → staging → consumption, and you know why shortcuts hurt
Incremental loads are not scary: watermarks, MERGE vs INSERT, you know the trade-offs
Git is just how you work: branches, PRs, code review — no big deal
You keep your tickets updated without being asked

Benefits

Comp & perks

**
Real problems, modern stack, no pointless busywork
Full ownership from day one — you own what you ship — and you build it so anyone on the team can pick it up tomorrow
A genuinely friendly team spread across different countries, continents, and time zones
Room to grow in any direction you want to take it
A place where AI tools are part of the culture, not a talking point.