Senior Data Engineer

Role overview

You will be a core member of a forward-deployed engagement team that onboards enterprise customers (often highly regulated, high-maturity organizations such as banks) to an AI Data Analyst SaaS platform. Your primary responsibility is to design and implement robust data models and connections so the AI Data Analyst can reliably compute business metrics and answer analytical questions. This is a high-touch role combining deep technical data modeling and engineering with stakeholder management, requirements gathering, testing/validation, risk mitigation, and project delivery.

What you’ll do — key responsibilities

• Customer liaison & discovery

• Lead discovery sessions with technical and non-technical stakeholders to understand source systems, data lineage, business definitions, and reporting needs.

• Map business KPIs/metrics to available data and identify gaps or remediation required.

• Data modeling & metric engineering

• Design logical and physical data models (facts, dimensions, hierarchies, slowly changing dimensions) that reflect customer business semantics and support the AI Data Analyst’s metric definitions.

• Define canonical metric specifications (metric definition, calculation SQL/DSL, cohort logic, edge cases).

• Platform integration

• Implement data connections, ingestion pipelines, and schema mappings into the SaaS platform (or customer’s cloud data layer) ensuring freshness, reliability, and observability.

• Configure dimensions, attributes, and metric metadata inside the platform so the AI models can consume and reason about the data.

• Validation & QA

• Develop and execute test plans to validate AI Data Analyst outputs against agreed-upon metric specs and ground-truth reports; quantify accuracy and identify root causes for discrepancies.

• Create automated and manual validation suites (unit tests, reconciliation queries, data quality checks).

• Project & stakeholder management

• Create project plans, manage timelines, set realistic expectations, and communicate status/risks to customers and internal stakeholders.

• Facilitate sign-offs on metric definitions, data readiness, and production cutovers.

• Risk, security & governance

• Identify data and model risks (PII exposures, inference errors, stale data) and put mitigation controls in place.

• Ensure implementations comply with customer security, data governance, and regulatory requirements.

• Knowledge transfer & documentation

• Produce clear runbooks, metric spec docs, and onboarding artifacts. Train customer users and internal support teams for ongoing operations.

• Continuous improvement

• Feed product/engineering with requirements and lessons learned to improve platform data modeling capabilities and onboarding playbooks.

Must-have qualifications

• 5+ years experience in data engineering or analytics engineering, with a strong focus on data modeling for enterprises (experience with banks or other highly regulated industries strongly preferred).

• Proven track record of translating business metric requirements into production-ready data models (fact/dimension modeling, SCD handling, hierarchies).

• Excellent stakeholder management with experience gathering requirements from both technical teams (ETL/analytics, data platform) and non-technical business teams (finance, product, ops).

• Strong SQL skills — able to author, optimize, and review complex analytic queries end-to-end.

• Experience validating analytical outputs and building reconciliation/QA processes.

• Demonstrable project management and expectation-management skills for customer engagements.

• Familiarity with data risk and governance concerns (PII handling, access controls, auditability).

• Excellent written and verbal communication skills; able to produce clear metric specs and runbooks.

Highly desirable (nice-to-have)

• Hands-on experience with modern cloud data stacks — AWS (S3, Glue, Redshift, Lambda), Databricks, or Snowflake.

• Experience building or architecting data lakes, Delta Lake, and streaming/batch pipelines.

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

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

• Experience working directly with enterprise security/compliance teams and implementing data access controls.

• Prior experience in a customer-facing or consulting/onboarding role for an analytics or ML product.

• Understanding of model evaluation and basic ML/LLM validation techniques (for AI output verification).

Core competencies & soft skills

• Customer-first mentality: patient, thorough, and able to build trust with enterprise stakeholders.

• Structured problem solving: break ambiguous business needs into measurable metric specs and test cases.

• Project management: scope, plan, manage trade-offs, and deliver with clear milestones.

• Risk & expectation management: proactively surface issues and propose mitigations.

• Collaboration: work closely with product, platform engineering, data science, and customer success.