About the Role
You will build and maintain the pipelines that facilitate the movement of data from the operational systems into the data warehouse, along with managing the data models that support all downstream processes. Your responsibilities will encompass ingestion, processing, and modeling, as well as ensuring proper documentation accompanies these tasks. Additionally, you will perform analyst tasks that ensure data integrity and accuracy.
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What You Will Do
- Profile new data sources before ingestion, which may include databases, APIs, files, open data, both structured and unstructured.
- Build, schedule, and monitor ingestion and processing pipelines utilizing existing tools and extend them as needed.
- Design and construct warehouse models (such as star schemas and conformed dimensions) that support dashboards and machine learning use cases.
- Implement data quality checks and lineage tracking within every pipeline, maintaining up-to-date documentation on conceptual, logical, and physical models.
- Collaborate with business owners to reconcile figures, define key performance indicators (KPIs), and assist in writing the monthly progress report.
What You Need
- Strong proficiency in Python and SQL with practical experience in production environments.
- Familiarity with Spark (Databricks or Hadoop) and BigQuery, along with comfort in handling both data lake and warehouse patterns.
- Hands-on experience with at least two tools from Talend, Informatica, NiFi, and Denodo.
- Experience in extracting and capturing changes from databases such as MS SQL Server, Oracle, MySQL, and PostgreSQL.
- Skills in ingesting data from APIs, files (including SFTP, Excel, CSV), and open data, performing both batch and streaming ingestion.
- Knowledge of dimensional modeling techniques including star schemas, conformed dimensions, and slowly changing dimensions, with the capability to document models at conceptual, logical, and physical levels.
- Experience with orchestration, monitoring, retries, data quality checks, and lineage tracking.
- Ability to conduct data profiling, reconciliation, and KPI definition in collaboration with business users.
Good to Have
- Familiarity with dbt, Airflow, Dataflow, and Iceberg.
- Google Professional Data Engineer or Databricks certification.
