Senior Data Engineer

MoldStud; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

MoldStud is seeking a data engineer to own and enhance an existing but reliable analytics stack. The role centers on keeping pipelines healthy, automating repetitive tasks with AI, and expanding data projects as the product grows.

You will work remotely from Moldova, collaborating in Romanian and English to ensure data accuracy and timely delivery. Responsibilities include managing ingestion from AWS S3 to Snowflake, tuning performance, extending dbt models, owning the Cube semantic layer, and

Qualifications

• 5+ years as a data engineer with production ownership.
• Experience: ingest from AWS S3 to Snowflake and load pipelines.
• Experience with dbt in production, performance, security and cost.
• Fluent English; Romanian language knowledge is a plus.

Responsibilities

• Keep pipelines and analytics stack running and on schedule.
• Identify and fix data quality issues and slow queries early.
• Ingest from AWS S3 into Snowflake with COPY commands and monitoring.
• Tune Snowflake for performance and cost efficiency.
• Maintain and extend dbt models, tests and deployments.
• Own the Cube semantic layer: definitions, versioning, deployments via GitHub.
• Manage Fivetran connectors and Hex integrations; track schedules & changes.
• Act as technical owner of data tools, including API keys and vendor interactions.
• Review Amplitude tracking plans and ensure consistent event landings.
• Build AI agents for monitoring, anomaly detection and schema change analysis.
• Explore and adopt new AI tools to speed up work; document procedures.

Tools

Snowflake

dbt

Fivetran

Cube

Hex

Metabase

Amplitude

AWS S3

Job description

This role is for a data engineer who likes owning a system that already works and making it work better. The stack is in place. Data lands in AWS S3 and loads into Snowflake. dbt models it, and a Cube semantic layer serves the metrics to APIs and Metabase dashboards. Fivetran and Hex sit around it, and product events arrive from Amplitude.

Most of your week goes into keeping that stack healthy. When a pipeline breaks or a number looks off, you are the first to know and the one who fixes it. The rest of the time goes into new data projects as the product grows, and into automating the repetitive parts of the job with AI agents.

You join the client's product and data team as one of its members, in its planning and its decisions. You work remotely from Moldova, in Romanian and English.

Critical Requirements

• Snowflake and dbt in production: you have run both on a live stack and looked after performance, security and cost, not only written models against them.
• AI in your daily workflow: LLMs and coding assistants are part of how you build, test and ship, and you can show the workflows or agents you built with them.
• Romanian and English: you work fluently in both, with English at C1 or higher.

Responsibilities

• Keep the pipelines and the analytics stack running day to day, so data arrives on time, correct and complete.
• Catch failures, slow queries and data quality problems early, and fix them before anyone downstream notices.
• Run the ingestion from AWS S3 into Snowflake: COPY commands, transformations and monitoring.
• Tune Snowflake for performance and help keep its cost down.
• Maintain and extend the dbt project: modelling standards, tests and deployments.
• Own the Cube semantic layer: metric definitions, versioning and deployments through GitHub, and metrics that match the warehouse models.
• Set up and watch the Fivetran connectors and Hex: sync schedules, dependencies, and what a schema or source change will break.
• Act as the technical owner of the data tools, from API keys and service accounts to the conversations with vendors.
• Look after Metabase: the key collections and saved queries, and a data model that matches Snowflake.
• Review the Amplitude tracking plans, and make sure events are defined consistently and land correctly in the warehouse.
• Build and maintain AI agents for routine work: pipeline monitoring, anomaly detection, schema change analysis and documentation.
• Try new AI tools, and bring in the ones that make the stack faster, cleaner or more reliable.
• Document pipelines, models, permissions and operating procedures, and help analysts use the data well.

Requirements

• 5+ years as a data engineer, with senior-level ownership of production data systems.
• Ingestion from cloud storage into a warehouse, especially AWS S3 to Snowflake.
• Fivetran or a similar ELT tool.
• A semantic layer such as Cube, or an equivalent.
• A BI tool such as Metabase or Looker.
• Data modelling, metrics governance and making data usable for analysts.
• Git-based workflows and production deployments.

Core Competencies

• Ownership: you notice problems before anyone reports them and see them through.
• Independence: you plan your own work and move it forward without daily direction.
• Clear writing: your documentation lets someone else run a pipeline without you.

Nice to Have

• A product analytics platform such as Amplitude.
• Warehouse cost optimization and planning how a warehouse scales.

Ownership: you notice problems before anyone reports them and see them through., Independence: you plan your own work and move it forward without daily direction., Clear writing: your documentation lets someone else run a pipeline without you.