Role Overview

In this role, you will be a key member of a customer-facing implementation team responsible for onboarding large enterprise clients—often in highly regulated, data-sensitive industries such as banking—onto our AI Data Analyst SaaS platform. Your mission is to design and implement reliable data models and integrations so the AI system can accurately compute business metrics and respond to analytical questions. This position blends deep technical data modeling and engineering expertise with stakeholder alignment, requirement discovery, testing, risk control, and project execution.

What You’ll Be Responsible For

Customer Engagement & Requirement Discovery

• Lead workshops and discussions with both business and technical stakeholders to analyze data sources, data lineage, business terminology, and reporting expectations.

• Connect business metrics and KPIs to system data, identifying any missing data or remediation needed to support them.

Data Modeling & Metric Design

• Create conceptual, logical, and physical data models (including facts, dimensions, hierarchies, SCDs) that represent core business logic and support AI metric calculations.

• Define standardized metric documentation, including business definition, SQL/DSL logic, cohort rules, and exceptional scenarios.

Platform Integration

• Build data connections, ingestion pipelines, and schema mappings into the platform (or customer cloud infrastructure) with a focus on freshness, reliability, and monitoring.

• Configure attributes, dimensions, and metric metadata so the AI system can understand and reason over the data.

Testing, Validation & Quality Assurance

• Develop and execute validation plans to compare AI Data Analyst outputs against official benchmarks/reports and track accuracy.

• Create automated and manual QA processes, including data checks, reconciliation queries, and testing suites.

Project & Stakeholder Coordination

• Prepare implementation timelines, communicate delivery expectations, and provide ongoing status/risk updates to internal and customer teams.

• Coordinate approvals for metric definitions, data quality standards, and go-live transitions.

Risk, Security & Compliance

• Detect and mitigate risks related to data privacy, model accuracy, stale or unreliable datasets, and PII exposure.

• Ensure the implementation follows customer policies on data security, governance, and regulatory standards.

Documentation & Enablement

• Produce clear onboarding materials, metric dictionaries/specs, and operational runbooks.

• Train customer teams and internal support resources for successful post-launch ownership.

Continuous Improvement

• Share product feedback and common onboarding challenges with internal teams to enhance data modeling capabilities and implementation playbooks.

Required Qualifications

• 5+ years in data engineering or analytics engineering, with strong enterprise data modeling experience (financial or regulated sectors strongly preferred).

• Proven ability to convert business metric needs into production-grade fact/dimension models, including SCD and hierarchy handling.

• Skilled at engaging both technical (data platform, ETL/analytics) and non-technical (finance, product, operations) stakeholders.

• Expert-level SQL skills, capable of constructing and tuning complex analytical queries.

• Experience validating analytical results and developing QA and reconciliation solutions.

• Strong project delivery and expectation management experience in customer environments.

• Understanding of data security, governance, and privacy practices (PII protections, access policies, auditability).

• Outstanding communication skills, with the ability to write clear metric documentation and operational guides.

Preferred (Nice to Have)

• Hands-on experience with modern cloud data ecosystems (AWS, Databricks, Snowflake).

• Background in building data lakes/lakehouses, Delta Lake, and streaming/batch pipelines.

• Familiarity with orchestration (Airflow, Prefect) and analytics engineering tools (dbt).

• Experience with Spark, Python (pandas/pySpark), event streaming (Kafka).

• Work experience involving enterprise security/compliance or implementing fine-grained data access controls.

• Prior customer-facing role in analytics, data platform, or ML/AI product onboarding.

• Basic knowledge of ML/LLM evaluation and validation practices.

Core Attributes & Soft Skills

• Customer-centric mindset: patient, empathetic, and able to establish trust with enterprise teams.

• Analytical and structured thinking: can turn open-ended business questions into precise metric definitions and test scenarios.

• Project leadership: able to scope, prioritize, and deliver work with clear milestones.

• Risk and expectation management: raises concerns early and proposes practical solutions.

• Collaboration: works effectively with product, engineering, data science, and customer success partners.