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We are hiring a mid to senior Analytics Engineer to join the Analytics & Data Engineering team at Ajaib. This role sits between data engineering and business stakeholders - owning the transformation layer that turns raw data into trusted, well-modeled datasets that power analytics, reporting, and ML use cases across the company. We're looking for someone who treats data ownership as a craft - not just shipping models, but standing behind them. In AE work, data correctness is something worth fighting for, and we want someone who pushes back when the numbers don't add up, follows the thread back to upstream engineers, and refuses to let root causes get patched over with workarounds. Key Responsibilities Design, build, and maintain dbt models on BigQuery, following modular and tested transformation patterns (staging → intermediate → marts). Take end-to-end ownership of assigned data domains — including the projects built on top of them, their data quality, documentation, and the trust stakeholders place in them. Treat data quality as non-negotiable: investigate anomalies, follow up persistently with data engineers and source system owners to resolve root causes rather than apply patches, and advocate for fixes upstream when needed. Partner with analysts, PMs, and business stakeholders to translate requirements into well-modeled datasets and clear metric definitions. Contribute to the team's governance frameworks — access control via taxonomy tags, naming conventions, model ownership, and CI/CD for dbt. Optimize BigQuery cost and performance (partitioning, clustering, incremental models, query patterns). Collaborate with data engineers on upstream pipelines (CDC, streaming, batch ingestion) to ensure source data fits downstream modeling needs. Proactively identify gaps and propose new AE initiatives — better testing frameworks, semantic layers, metric stores, internal tooling, automation — when current work feels routine. We want someone who gets restless and turns that into innovation, not someone who waits for tickets. Mentor junior analytics engineers and review PRs. Requirements 5+ years of experience as an Analytics Engineer, Data Engineer, or Data Analyst with strong SQL/modeling focus. Strong SQL skills, including window functions, CTEs, and query optimization on a cloud warehouse. Production experience with dbt (sources, models, tests, macros, snapshots, exposures). Hands-on experience with BigQuery or an equivalent cloud data warehouse (Snowflake, Redshift). Solid understanding of dimensional modeling (Kimball) and modern data modeling patterns. Experience with version control (Git) and CI/CD workflows for data pipelines. Ability to communicate clearly with both technical and non-technical stakeholders, including holding the line on data quality issues with upstream teams. A track record of self-directed initiatives — projects, tools, or improvements you started without being asked. Nice to Have Experience in fintech, investment, or financial services domains. Familiarity with streaming/CDC pipelines (Flink, Kafka, Spanner Change Streams, Debezium). Experience with data governance frameworks (column-level security, PII handling, data catalogs). Python for data tooling and orchestration (Airflow, Dagster, or similar). Exposure to BI tools (Looker, Metabase, Tableau). Experience working with product analytics events (Amplitude, Mixpanel, Segment). Tech Stack BigQuery, dbt, Airflow, Git/GitHub Actions.