[1335] Senior Data Engineer

Job Details

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.

Job Requirements

• 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).