BI Data Analytics Engineer

Role Overview

This position is central to how the business consumes trustworthy data: you will own the pipelines that move information from source systems into a cloud warehouse, shape it into analysis-ready models, and keep the whole flow observable and reliable. Day to day, that means writing production-grade Python for custom API connectors, modelling transformed data in dbt, and orchestrating scheduled jobs with Dagster, all so the BI team can build reports that leadership actually bases decisions on. You are not just a SQL-focused pipeline builder; you are the engineer responsible for the ingestion layer end to end, including the integrations that no off-the-shelf connector can handle.

Key Responsibilities

• Design, build, and maintain data pipelines that ingest data from diverse source systems, using Airbyte, dbt, dlt, and Dagster as the core toolchain.
• Develop custom API integrations for platforms without pre-built connectors, including payment providers, mobile-money services, PayGo systems, CRMs, and internal applications, handling authentication, pagination, rate limits, and retries.
• Implement transformation logic in dbt and SQL that turns raw ingested data into well-structured, documented, and performant models for analytics and reporting.
• Build and operate monitoring, alerting, and automated data-quality checks so that freshness, volume, and accuracy issues are detected before they reach downstream stakeholders.
• Collaborate with BI analysts and cross-functional stakeholders to understand reporting requirements and translate them into resilient data infrastructure.
• Manage analytics code through Git-based workflows with CI/CD, promoting changes safely across development, staging, and production environments.
• Document pipeline architecture, data contracts, and operational runbooks so the team can scale without relying on tribal knowledge.
• Proactively identify opportunities to improve warehouse performance, reduce cost, and simplify existing ELT processes.

Requirements & Qualifications

• Three or more years of hands-on experience in analytics engineering or data engineering, with a clear track record of delivering production pipelines.
• Strong command of data warehousing concepts including dimensional modeling, incremental loading, partitioning, and query performance tuning.
• Expert-level SQL for analytical workloads, including complex transformations, window functions, and optimization for cloud warehouses.
• Solid Python skills for writing and maintaining data pipelines, API clients, and custom transformation logic that goes beyond simple SQL.
• Practical experience with modern ELT and orchestration tools such as dbt, Dagster, Airbyte, dlt, or comparable alternatives.
• Working knowledge of a cloud data warehouse, ideally Redshift, with attention to both query performance and cost implications.
• Hands-on experience with data-quality tooling and testing approaches, such as dbt tests or Great Expectations, plus familiarity with pipeline observability and alerting.
• Comfortable with Git, CI/CD, and environment management for analytics code, including peer review and automated deployment.
• Proven ability to build and maintain API integrations involving OAuth2 or token-based auth, pagination, rate limiting, and incremental or event-driven ingestion.
• Willingness to use AI-assisted development tools to accelerate coding, testing, and documentation, while still owning the quality of the output.