Drive the design, development, and optimization of scalable data solutions that power analytics, reporting, and strategic decision-making.
Build and optimize robust ETL/ELT pipelines for large structured datasets using SQL and Python (Pandas, PySpark).
Analyze complex datasets to generate actionable insights for business stakeholders.
Develop high-quality analytical datasets and reusable data models to ensure analysis-ready data.
Perform data cleansing, transformation, and validation using advanced techniques including regular expressions.
Write and optimize complex BigQuery queries for analytical exploration and production reporting.
Integrate external data sources through APIs and web scraping to automate ingestion workflows.
Design, maintain, and enhance scheduled pipelines using Apache Airflow.
Apply big data processing, distributed computing, and performance optimization best practices.
Architect scalable and secure data solutions on cloud platforms (preferably GCP BigQuery, Cloud Storage, Composer, Dataflow).
Lead data engineering best practices including version control, CI/CD, data quality frameworks, and governance standards.
Design and maintain interactive dashboards and visualizations using Tableau or Power BI.
Manage end-to-end analytical deliverables from requirement gathering through final reporting.
Collaborate with business, product, and analytics teams to translate requirements into technical solutions.
Mentor junior engineers and establish coding standards, documentation practices, and reusable frameworks.
Support integration of data workflows into user-facing applications with understanding of HTML, CSS, JavaScript, React, or Angular.
Lead complex data initiatives and drive architectural decisions in Agile environments.
10+ years of experience in data engineering with strong expertise in SQL, Python, Pandas, PySpark, and distributed data systems.

Group Manager

WNS

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