Python Data & Automation Engineer_AWS_ETL_SQL

Job Description: Python Data & Automation Engineer (AWS_SQL_ETL / ELT Focused)

Experience: 3 8 Years

Location: Noida / Gurugram / Bangalore / Pune

Notice Period: Immediate Joiner / Serving Notice Period

Employment Type: Full-time

Role Overview

We are looking for an experienced Python Data & Automation Engineer with a primary focus on data engineering, ETL/ELT pipelines, SQL, and process automation—not backend web development. In this role, you will be responsible for creating robust database connections, extracting data via internal and external APIs, running high-performance data transformations using Pandas and NumPy, and automating pipelines across on-premises and AWS cloud environments.

Key Responsibilities

ETL/ELT & Data Pipelines: Design, build, and optimize scalable ETL/ELT pipelines; automate the ingestion, cleansing, and validation of large structured and unstructured datasets.

Data Processing & Analytics: Build high-performance data manipulation, aggregation, and numerical computation routines using Pandas and NumPy.

Database Connectivity & SQL: Establish connections with relational and analytical databases using PyODBC and native connectors; write, tune, and optimize complex SQL queries, views, and stored procedures.

API Integration & Automation: Design and deploy automated extraction pipelines interacting with REST/SOAP APIs to eliminate manual reporting and batch processing.

Cloud & Modern Data Platforms: Integrate automated data workflows into AWS (specifically S3 and EC2) and cloud data warehouses like Snowflake.

Legacy System Migration: Support, integrate, and modernize legacy workflows, including migration touchpoints from SAS to modern Python pipelines.

Version Control & CI/CD: Utilize GitHub / Git for version control, code reviews, and pipeline automation.

Technical Skills & Qualifications

Must-Have Skills:

Experience: 3 to 8 years of direct hands-on experience in data engineering, ETL/ELT pipeline development, and data automation (not pure backend web/Django frameworks).

Python for Data: Deep expertise in Python scripting with a primary focus on Pandas, NumPy, and automation utilities.

Database & Connectivity: Strong SQL proficiency along with hands-on experience using PyODBC or similar database drivers.

API Data Ingestion: Proven ability to extract and ingest data from RESTful APIs and web services.

Cloud & Tooling: Hands-on experience with AWS (especially S3, EC2) and version control using GitHub.

Preferred / Good-to-Have Skills:

Snowflake: Hands-on experience with Snowflake querying, architecture, or data loading.

SAS Integration: Prior experience working with or migrating legacy SAS scripts into Python.

Workflow Scheduling: Familiarity with orchestration and scheduling tools like Apache Airflow or Cron.