Data Engineer

Role Overview

Elevate is hiring a Data Engineer to build the data infrastructure behind its next-generation products and internal platforms. The role focuses on scalable data lakes/warehouses, ingestion and transformation pipelines, data quality, observability, and the operating practices that keep data products trustworthy over time.

Responsibilities

• Build and maintain data infrastructure for new and existing products (data lakes, warehouses, pipelines, data services).
• Develop ETL/ELT pipelines to ingest, clean, transform, validate, and deliver data from partner systems, client sources, internal apps, and third-party providers.
• Implement data modeling and transformation logic for data products, reporting automation, BI, and future data needs.
• Ensure pipeline reliability with monitoring, alerting, and recovery patterns.
• Create data quality rules, validation checks, schema management, and reconciliation workflows.
• Produce practical documentation covering sources, transformations, ownership, lineage, and operational runbooks.
• Collaborate with product engineers, analytics teams, ML engineers, and business stakeholders to turn data requirements into maintainable systems.
• Recommend whether a data request should become platform infrastructure vs. a one-off analysis vs. not worth the build/maintenance cost.

Requirements

• 4+ years of experience in data engineering, analytics engineering, backend/data platform engineering, or a closely related role.
• Strong SQL and Python for building production-grade data pipelines.
• Experience designing/operating data lakes and data warehouses, ETL/ELT workflows, and batch/scheduled jobs.
• Experience with cloud data infrastructure, especially AWS services (e.g., S3, Glue, Lambda, Step Functions, ECS, Redshift, Athena, RDS, EventBridge).

Nice-to-Haves

• Not explicitly stated in the provided text.