Analytics Engineer
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At a glance
Job overview
Flutterwave is hiring an Analytics Engineer. Flutterwave seeks an Analytics Engineer to own the transformation layer, building ingestion pipelines with Estuary, modeling data in dbt, and writing performant SQL and Python on Amazon Redshift to deliver accurate, fast, self‑serve analytics.
Key focus areas include Build and maintain modular tested documented dbt models, Build and monitor real-time batch ingestion pipelines using Estuary, and Write performant SQL and design dimensional models in Redshift.
Important skills include Amazon Redshift, Data Modeling, Data Quality, Data Governance, Airflow, and dbt Modeling. Preferred (not required): Fivetran, Airbyte, Debezium, and Kafka.
Skills & qualifications
Skills
Qualifications
Full job description
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 o wn 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
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Minimum 3+ years in analytics engineering, data engineering, or a similar role.
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Strong production SQL (window functions, CTEs, performance tuning)
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Hands-on dbt experience in production
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Proficiency in Python for data workflows and automation
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Experience with Amazon Redshift (dist/sort keys, WLM, tuning)
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Experience with ELT/CDC ingestion tooling — Estuary or comparable (Fivetran, Airbyte, Debezium, Kafka)
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Solid data modeling fundamentals (Kimball dimensional modeling, star schemas)
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Comfortable with Git-based version control and CI/CD
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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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