Cloud Data Engineer

Revenue Operations Cloud Data Engineer Location: Remote Reports To: Head of AI/ML/Data The Role Build and own Client's revenue data infrastructure — the pipelines, warehouses, and analytics platforms that power how the firm measures, forecasts, and grows revenue across wealth management. Design cloud-native data systems on AWS that unify advisor activity, client lifecycle, billing, AUM flows, and business performance into a single source of truth. This is a hands-on engineering role requiring deep expertise in data pipeline architecture, cloud data warehousing, observability, and the operational data needs of a high-growth financial services business. Your Impact Design and maintain production data pipelines that ingest, transform, and deliver revenue, billing, AUM, and advisor performance data Build and optimize Redshift-based warehouse architecture supporting analytics, forecasting, and operational reporting Create Grafana dashboards and alerting for revenue KPIs, pipeline health, data freshness, and system observability Develop ML-ready datasets and feature pipelines in SageMaker to support revenue forecasting, advisor capacity planning, and client segmentation Build automated data quality frameworks — validation, anomaly detection, lineage tracking, and SLA monitoring Integrate data from CRM, custodial platforms, billing systems, advisor tools, and third-party sources into a unified revenue data model Partner with Finance, Growth, Advisor Success, and Product to translate business questions into reliable, self-serve data products The Ideal Match 5+ years building production data pipelines and warehouse infrastructure Expert-level Python for ETL/ELT pipelines, data transformations, API integrations, and automation Deep Redshift experience: schema design, query optimization, materialized views, distribution/sort key strategy, and workload management Production PostgreSQL expertise: performance tuning, indexing, partitioning, and managing analytical workloads alongside transactional systems Hands-on with Glue, Step Functions, Lambda, S3, EventBridge, and IAM for orchestrating and securing data workflows Strong SQL across complex multi-source joins, window functions, CTEs, and incremental load patterns Data modeling fundamentals: dimensional modeling, slowly changing dimensions, fact/dimension tables, and schema evolution Operational rigor: monitoring, alerting, incident response, and on-call ownership for data infrastructure Strong cross-functional communication — translating between engineering, finance, and business stakeholders Bonus Points Infrastructure-as-code CDK/CloudFormation) for managing data platform resources Streaming and near-real-time data processing: Kinesis, Kafka, or DynamoDB Streams dbt or similar transformation frameworks for managing warehouse logic as code Financial services data: AUM calculations, fee/billing reconciliation, custodial data feeds, advisor compensation modeling Data governance and compliance: PII handling, access controls, audit logging, and regulatory data retention Apache Airflow for complex DAG orchestration Cost optimization for Redshift and S3-based data lake architectures at scale