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