Assistant Manager

Core Technical Skills • Strong Python programming experience • Advanced SQL • DBT development • Google BigQuery • PySpark • Data engineering and ETL/ELT pipeline development • Data validation and reconciliation • Data migration experience Python Libraries & Frameworks • Pandas • NumPy • PyArrow • Pytest • Great Expectations or similar data quality framework • Google Cloud Python SDK GCP Services • BigQuery • Cloud Storage • Cloud Composer / Airflow • Dataproc • Dataflow • IAM • Cloud Logging and Monitoring Lead smallscale data engineering projects. Ensure stable data processing operations. Mentor junior engineers. Provide project support. SAS Migration & Code Conversion • Analyse existing SAS datasets, SAS programs, PROC SQL, Data Steps, and SAS macros. • Convert SAS business logic into Python, SQL, PySpark, and DBT models. • Support migration of historical and incremental SAS data into GCP. • Document SAS-to-GCP transformation logic and technical conversion rules. • Assist in identifying reusable patterns for SAS macro conversion. Python Data Engineering • Develop Python-based data processing, automation, and validation scripts. • Build reusable Python frameworks for data ingestion, transformation, and reconciliation. • Use Python libraries such as Pandas, NumPy, PyArrow, and Google Cloud SDK. • Automate data quality checks, file processing, metadata extraction, and exception reporting. • Develop utilities to compare SAS outputs with GCP outputs. DBT & BigQuery Development • Develop and maintain DBT models for transformation logic. • Convert SAS PROC SQL logic into DBT SQL models. • Implement DBT tests for data quality, uniqueness, referential integrity, and business rules. • Support Bronze, Silver, and Gold layer implementation in BigQuery. • Optimize SQL queries and BigQuery tables for performance and cost efficiency. GCP Data Pipeline Development • Build scalable data pipelines using GCP services such as: • BigQuery • Cloud Storage • Cloud Composer / Airflow • Dataproc • Dataflow • Cloud Functions • Develop batch and incremental data processing workflows. • Support orchestration, monitoring, and production deployment of pipelines. • Implement secure data movement from legacy SAS platforms to GCP. Data Quality, Validation & Reconciliation • Perform source-to-target reconciliation between SAS and GCP. • Validate record counts, financial totals, business rules, and derived metrics. • Investigate and resolve data mismatches and transformation defects. • Build automated reconciliation reports using Python and SQL. • Support SIT, UAT, regression testing, and production validation.