Data Engineer

Job Description • The Data Engineer will support a strategic data platform migration initiative, building the data pipelines and warehouse infrastructure required to transition business-critical operations from a third-party SaaS CRM (Salesforce) onto an internal entity-modeled data platform. • The engineer will design and maintain bidirectional ETL flows between Salesforce, distributed data warehouses, and operational entity stores, ensuring data consistency, freshness, and auditability throughout the migration. • Work spans pipeline development, schema design, data quality engineering, and orchestration of scheduled batch and streaming jobs. Job Responsibilities • Design and build bidirectional data pipelines between Salesforce, distributed data, and internal operational entity-modeled data stores. • Build streaming data pipelines using distributed event-streaming systems (Kafka or equivalent pub/sub log architectures) for real-time data propagation. • Design and maintain dimensional and entity schemas in the internal data platform, including access permissions, mutation rules. • Build data validation, reconciliation, and quality monitoring frameworks to ensure parity between legacy and replacement systems during transition. • Develop and maintain data documentation including schema definitions, transformation logic, source-to-target mappings, and data dictionaries. • Use AI-assisted development tools (e.g., Claude Code, Cursor, Copilot) as a core part of the development workflow to accelerate pipeline development, SQL authoring, and schema migrations. • Partner with software engineers, Salesforce administrators, and business stakeholders to define data contracts, SLAs, and migration cutover criteria. • Investigate and resolve data quality incidents, pipeline failures, and reconciliation discrepancies. Skills • Experience with Hack/PHP coding and GraphQL. • Strong proficiency in SQL. • Comfort with ORM frameworks. • Hands-on experience building and operating large-scale ETL/ELT pipelines in distributed data warehouse environments (Hive, Spark, Presto/Trino, BigQuery, Snowflake, or Databricks). • Familiarity with distributed event-streaming systems (Kafka, Pulsar, Kinesis, or equivalent) for real-time data ingestion. • Hands-on experience using AI coding assistants (Claude Code, Cursor, GitHub Copilot, etc.) as part of a daily development workflow. • Strong understanding of data quality engineering -- testing, validation, monitoring, and reconciliation patterns. • Experience with data migrations between heterogeneous systems is highly desirable. • Verbal and written communication skills, problem solving skills, customer service and interpersonal skills. • Strong ability to work independently and manage one's time. • Strong ability to troubleshoot data issues and make system changes as needed to resolve them. Education/Experience • Bachelor's degree in computer science, data engineering, information systems, or relevant field required. • 3+ years of professional data engineering experience preferred. Typical Day-to-Day in the role • Perform data migration of all tables (approximately 80 objects of data) from Salesforce to the internal data platform. • Clean up and restructure data to improve organization and usability. • Define and implement data architecture decisions for how data should be structured in the new system. • Prepare and transform data to be AI-ready for downstream tooling and applications. • Build out and maintain the data management layer using internal tools including Scribe (Client's internal Kafka equivalent). Must-Have Skills • Entity/schema modeling and GraphQL. • ORM-framework & MySQL. • Real time data propagation (Kafka, Pulsar, Kinesis, or equivalent). Nice-to-have Skills • Salesforce Knowledge. • AI development workflows. • Events and Subscription handling (Iris).