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Assistant Manager

EXL

Posted 19 Oct 2026

BengaluruHigh payGreat Place to Work
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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

Responsibilities

  • Lead smallscale data engineering projects. Ensure stable data processing operations. Mentor junior engineers. Provide project support.

Qualifications

  • 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.