Data Engineer
Role Overview
Build and maintain pipelines moving data from client operational systems into a warehouse, along with the models and documentation used downstream.
The role also supports analyst work to validate figures and business metrics.
Key Skills for This Role
Full Job Posting
Role overview
Build and maintain pipelines moving data from client operational systems into a warehouse, along with the models and documentation used downstream.
The role also supports analyst work to validate figures and business metrics.
Responsibilities
- Profile new database, API, file, and open-data sources, including structured and unstructured data.
- Build, schedule, and monitor ingestion and processing pipelines using existing tools, extending them where useful.
- Design warehouse models, including star schemas and conformed dimensions, for dashboards and machine-learning use cases.
- Add data-quality checks and lineage capture to pipelines and maintain conceptual, logical, and physical model documentation.
- Work with business owners to reconcile figures, define KPIs, and help prepare monthly progress reports.
Requirements
- Strong Python and SQL used in production.
- Spark with Databricks or Hadoop, BigQuery, and familiarity with data-lake and warehouse patterns.
- Hands-on experience with at least two of Talend, Informatica, NiFi, and Denodo.
- Extracting and capturing changes from MS SQL Server, Oracle, MySQL, and PostgreSQL.
- Ingesting batch and streaming data from APIs, SFTP, Excel, CSV, and open data.
- Dimensional modeling with star schemas, conformed dimensions, and slowly changing dimensions; document models at conceptual, logical, and physical levels.
- Orchestration, monitoring, retries, data-quality checks, lineage, profiling, reconciliation, and defining KPIs with business users.
Good to have
- dbt, Airflow, Dataflow, and Iceberg are listed as good to have.
- Google Professional Data Engineer or Databricks certification is listed as good to have; the source text ends mid-word after “certificatio”.
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