Flutterwave was founded on the principle that every African must be able to participate and thrive in the global economy. To achieve this objective, we have built a trusted payment infrastructure that allows consumers and businesses (African and International) make and receive payments in a convenient borderless manner.
The role: Flutterwave is looking for an analytics engineer who will own the transformation layer that turns raw, streaming, and batch data into clean, tested, and well-documented datasets. Build ingestion pipelines with Estuary, model data in dbt, and write performant SQL and Python on Amazon Redshift to make analytics accurate, fast, and self-serve.
Responsibilities include, but are not limited to:
- dbt modeling: Build and maintain modular, tested, documented dbt models (staging → intermediate → marts); manage tests, macros, and materializations.
- Estuary pipelines: Build and monitor real-time and batch ingestion, including CDC from operational sources; handle schema evolution, backfills, and connector issues.
- Redshift & SQL: Write performant SQL and design dimensional models; tune queries and warehouse performance (dist/sort keys, WLM, cost optimization).
- Python: Automate transformations, orchestration hooks, custom dbt macros, and API integrations.
- Data quality & testing: Establish tests, freshness checks, monitoring, and alerting; own CI/CD for analytics code via Git.
- Documentation & semantics: Maintain model and metric documentation and a consistent semantic layer so definitions stay single-sourced.
- Collaboration: Partner with analysts, data scientists, and business teams to translate requirements into scalable, trustworthy datasets.
- Governance: Apply access controls, PII handling, and data privacy practices (e.g., GDPR, SOC 2).
Required competency and skillset to be a waver
- Minimum 3+ years in analytics engineering, data engineering, or a similar role.
- Strong production SQL (window functions, CTEs, performance tuning)
- Hands-on dbt experience in production
- Proficiency in Python for data workflows and automation
- Experience with Amazon Redshift (dist/sort keys, WLM, tuning)
- Experience with ELT/CDC ingestion tooling — Estuary or comparable (Fivetran, Airbyte, Debezium, Kafka)
- Solid data modeling fundamentals (Kimball dimensional modeling, star schemas)
- Comfortable with Git-based version control and CI/CD
- Authorization to work in the country without sponsorship
Preferred qualifications include
- Real-time / streaming and CDC architecture experience
- Orchestration tools (Airflow, Dagster, Prefect, dbt Cloud)
- BI tools and semantic layers (Looker, Tableau, Power BI)
- Data observability (Elementary, Great Expectations)
- AWS ecosystem familiarity (S3, Glue, IAM), Docker
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