Senior Data Software Engineer with AWS and Terraform

We are seeking a Senior Data Software Engineer to join a client-facing delivery team building and hardening cloud-native data pipelines on AWS as part of a data platform modernization program. The role involves ingesting and transforming large datasets with PySpark on AWS Glue and delivering curated, validated data into Snowflake, with a core focus on data quality, validation, and reconciliation for downstream analytics. This position is delivered at a Senior Consultant level with high autonomy and direct client stakeholder communication.

Responsibilities

• Design, build, and optimize scalable batch and incremental ETL/ELT pipelines using PySpark on AWS Glue
• Configure Glue jobs, crawlers, triggers, connections, bookmarks, workflows, and the Glue Data Catalog
• Tune workers, partitioning, and shuffle behavior for cost and performance optimization
• Model and load curated datasets into Snowflake with staging, transformation, and publishing layers
• Implement automated data quality and validation frameworks, including schema/contract enforcement and null/uniqueness/referential checks
• Develop row-count and financial reconciliation processes, anomaly detection, and quarantine/reject handling
• Configure and extend Glue Data Quality (DQDL) rules per requirements
• Write clean, modular, testable Python with unit/integration tests and reusable libraries
• Integrate pipelines with AWS services such as S3, IAM, Lambda, Athena, CloudWatch, Step Functions, and Secrets Manager
• Instrument observability through logging, metrics, alerting, and pipeline SLA monitoring
• Participate in code reviews, CI/CD automation, and documentation
• Engage directly with client stakeholders in requirements refinement, design walkthroughs, status reporting, and act as technical advisor within the workstream

Requirements

• 3+ years of experience with Python for production-level data engineering, including OOP and functional patterns
• Expertise in PySpark for distributed data processing and the DataFrame API
• Advanced proficiency in Snowflake, including data warehousing and staging/transformation layers
• Skills in AWS Glue, including job configuration, crawlers, Data Catalog, and DQDL
• Background in data quality engineering, including validation frameworks and reconciliation
• Proficiency in AWS services including S3, IAM, Lambda, Athena, and CloudWatch
• English proficiency at B2 level or higher

Nice to have:

• Familiarity with Generative AI / LLM concepts
• Knowledge of Airflow / Step Functions orchestration
• Familiarity with Great Expectations or similar data quality frameworks
• Knowledge of Terraform / CloudFormation