About the Role

You’ll build, operate, and evolve the end-to-end data platform that powers analytics, automation, and AI use cases. This is a hands-on role spanning cloud infrastructure, ingestion/ETL, and data modeling across a Medallion (bronze/silver/gold) architecture. You’ll partner directly with stakeholders to turn messy source data into trusted datasets, metrics, and data products.

Who you are

• Pragmatic Builder: You write clear SQL/Python, ship durable systems, and leave pipelines more reliable than you found them.

• Data-Savvy Generalist: You’re comfortable moving up and down the stack (cloud, pipelines, warehousing, and BI) and picking the right tool for the job.

• Fundamentals-first & Customer-Centric: You apply strong data modeling principles and optimize the analyst/stakeholder experience through consistent semantics and trustworthy reporting.

• Low-Ego, High-Ownership Teammate: You take responsibility for outcomes, seek feedback openly, and will roll up your sleeves to move work across the finish line.

• High-Energy Communicator: You’re comfortable presenting, facilitating discussions, and getting in front of stakeholders to drive clarity and alignment.

• Self-Starter: You unblock yourself, drive decisions, and follow through on commitments; you bring a strong work ethic and invest in continuous learning.

What you will do

• Ingestion & ETL: Build reusable ingestion and ETL frameworks (Python and Spark) for APIs, databases, and un/semi-structured sources; handle JSON/Parquet and evolving schemas.

• Medallion Architecture: Own and evolve Medallion layers (bronze/silver/gold) for key domains with clear lineage, metadata, and ownership.

• Data Modeling & Marts: Design dimensional models and gold marts for core business metrics; ensure consistent grain and definitions.

• Analytics Enablement: Maintain semantic layers and partner on BI dashboards (Sigma or similar) so metrics are certified and self-serve.

• Reliability & Observability: Implement tests, freshness/volume monitoring, alerting, and runbooks; perform incident response and root-cause analysis (RCA) for data issues.

• Warehouse & Performance: Administer and tune the cloud data warehouse (Snowflake or similar): compute sizing, permissions, query performance, and cost controls.

• Standardization & Automation: Build paved-road patterns (templates, operators, CI checks) and automate repetitive tasks to boost developer productivity.

• AI Readiness: Prepare curated datasets for AI/ML/LLM use cases (feature sets, embeddings prep) with appropriate governance.

Must Haves

• 3–5+ years hands-on data engineering experience; strong SQL and Python; experience building data pipelines end-to-end in production.

• Strong cloud fundamentals (AWS preferred; other major clouds acceptable): object storage, IAM concepts, logging/monitoring, and managed compute.

• Experience building and operating production ETL pipelines with reliability basics: retries, backfills, idempotency, incremental processing patterns (e.g., SCDs, late-arriving data), and clear operational ownership (docs/runbooks).

• Solid understanding of Medallion / layered architecture concepts (bronze/silver/gold or equivalent) and experience working within each layer.

• Strong data modeling fundamentals (dimensional modeling/star schema): can define grain, build facts/dimensions, and support consistent metrics.

• Working experience in a modern cloud data warehouse (Snowflake or similar): can write performant SQL and understand core warehouse concepts.

• Hands-on dbt experience: building and maintaining models, writing core tests (freshness/uniqueness/RI), and contributing to documentation; ability to work in an established dbt project.

• Experience with analytics/BI tooling (Sigma, Looker, Tableau, etc.) and semantic layer concepts; ability to support stakeholders and troubleshoot issues end-to-end.

Nice to Have

• Snowflake administration depth: warehouse sizing and cost management, advanced performance tuning, clustering strategies, and designing RBAC models

• Advanced governance & security patterns: masking policies, row-level security, and least-privilege frameworks as a primary implementer/owner

• Strong Spark/Py Spark proficiency: deep tuning/optimization and large-scale transformations.

• dbt “platform-level” ownership: CI/CD-based deployments, environment/promotion workflows, advanced macros/packages, and leading large refactors or establishing standards from scratch.

• Orchestration: Airflow/MWAA DAG design patterns, backfill strategies at scale, dependency management, and operational hardening

• Sigma-specific depth: semantic layer/metrics layer architecture in Sigma, advanced dashboard standards, and organization-wide “certified metrics” rollout.

• Automation / i PaaS experience: Workato (or similar) for business integrations and operational workflows.

• Infrastructure-as-code: Terraform (or similar) for data/cloud infrastructure provisioning, environment management, and safe change rollout.

• Data observability & lineage tooling: Open Lineage/Monte Carlo-style patterns, automated lineage hooks, anomaly detection systems.

• Lakehouse / unstructured patterns: Parquet/Iceberg, event/data contracts, and advanced handling of semi/unstructured sources.

• AI/ML/LLM data workflows: feature stores, embeddings/RAG prep, and privacy-aware governance.

At Abnormal AI, certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

Base salary range:
$123,300—$145,000 USD

Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.