Data Engineer Analyst

Blend360

About The Position

Blend is seeking a Data Engineer Analyst to support its growth and expansion. The role designs, builds, and maintains scalable data pipelines and ingestion frameworks for batch processing, streaming, and change data capture (CDC). It develops and optimizes Apache Spark and PySpark workflows to transform large datasets reliably and efficiently. The position implements and manages AWS data lake architectures, using Amazon S3 as the primary storage layer and Apache Iceberg for table management, schema evolution, and time travel. It also uses AWS Glue for ETL orchestration, Athena for serverless queries, Lambda for event-driven processing, and IAM for secure, least-privilege access. The engineer collaborates with analysts, data scientists, and platform engineers to deliver production-ready solutions. The role includes monitoring pipeline health, troubleshooting data quality issues, improving data reliability and freshness, and documenting data flows, architecture decisions, and engineering standards.

Requirements

At least 2 years of experience in Data Engineering or related roles, with hands-on experience building and maintaining cloud-based data pipelines and lake architectures on AWS.
Advanced hands-on experience with AWS Glue, Amazon S3, Amazon Athena, and AWS IAM.
Experience with Apache Spark and PySpark for data processing.
Experience with data lake architectures; knowledge of Apache Iceberg is preferred.
Experience with batch, streaming, and CDC data ingestion patterns.
Experience with SQL for querying, validation, and exploratory analysis.
Solid understanding of data modeling and large-scale data processing.
Ability to collaborate across functions and communicate technical concepts clearly to non-technical stakeholders.
Advanced English proficiency, required for effective communication with global teams.

Nice To Haves

Experience with infrastructure-as-code or pipeline orchestration tools such as Airflow, Step Functions, or dbt.
Familiarity with AI-assisted development workflows and engineering productivity tools.
Knowledge of Apache Iceberg.

Responsibilities

Design, build, and maintain scalable data pipelines and ingestion frameworks for batch processing, streaming, and CDC.
Develop and optimize data processing workflows using Apache Spark and PySpark.
Implement and manage AWS data lake architectures using S3 and Apache Iceberg.
Use AWS Glue, Athena, and Lambda for ETL orchestration, serverless querying, and event-driven data processing.
Manage data access controls and security configurations using AWS IAM.
Collaborate with data analysts, data scientists, and platform engineers to understand requirements and deliver production-ready solutions.
Monitor pipeline health, troubleshoot data quality issues, and improve data reliability and freshness.
Document data flows, architecture decisions, and engineering standards.

Benefits

Flexible working options.
Equipment provided, including a MacBook and accessories.
Learning opportunities, including AWS certifications, study plans, courses, other certifications, English lessons, and Tech Tuesdays.
Mentoring and development opportunities.
Anniversary and birthday gifts.
Daily lunches, gourmet Friday meals, snacks, and beverages at headquarters.
After-office events and recreational activities at headquarters, including sports, games, and music nights.

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