Data Engineer – Platform & Analytics Infrastructure

Immediate Hiring – Walbrydge Technologies

Role Overview

Walbrydge Technologies is hiring Data Engineers to design and build the core data infrastructure that powers real-time analytics, operational intelligence, and machine learning pipelines across our enterprise ecosystem.

This role is critical to enabling high-quality, scalable, and reliable data platforms that support real-time dashboards, analytics, and future AI-driven capabilities.

Position Details

Role: Data Engineer – Platform & Analytics Infrastructure
Experience: 5–8 Years
Positions: 3
Location: Hyderabad, India(Offshore)
Employment Type: Full-Time
Start Date: 1-2 Weeks

Interested candidates may send their resume to: info@walbrydge.com

Key Responsibilities

Data Platform & Pipeline Engineering

• Design and implement end-to-end data pipelines supporting both batch and real-time streaming workloads.
• Build and maintain ETL / ELT workflows using modern data engineering tools.
• Architect and manage data lakes, data warehouses, and real-time event pipelines.

Data Ingestion & Processing

• Develop scalable ingestion pipelines from distributed microservices, APIs, and event streams.
• Implement real-time data processing using Kafka, Spark Streaming, Flink, or equivalent technologies.
• Optimize pipelines for latency, throughput, and fault tolerance.

Data Quality, Governance & Optimization

• Implement data quality checks, validation rules, lineage tracking, and governance standards.
• Design efficient data models, partitioning strategies, and schema evolution.
• Optimize storage and compute for performance, scalability, and cost efficiency.

Cross-Functional Collaboration

• Work closely with ML Engineers, Data Scientists, Backend Engineers, and DevOps teams.
• Support analytics, reporting, and ML feature pipelines with clean, reliable datasets.
• Ensure security, access control, and compliance across all data systems.

Required Skills & Experience

Core Technical Skills

• 5–8 years of professional experience in Data Engineering.
• Strong proficiency in Python and SQL.
• Hands-on experience with ETL/ELT orchestration tools such as Airflow, dbt, or similar.
• Experience building streaming and event-driven pipelines using Kafka, Kinesis, Spark Streaming, or Flink.

Data Storage & Analytics Platforms

• Strong experience with data warehouses such as:
• Snowflake, Redshift, BigQuery, Azure Synapse, or Databricks
• Hands-on experience with data lakes:
• AWS S3, Azure Data Lake (ADLS), or Google Cloud Storage (GCS)

Platform & Architecture Knowledge

• Strong understanding of data modeling, schema design, partitioning, and indexing.
• Experience with API-based ingestion and real-time event streaming.
• Familiarity with CI/CD pipelines, containerization, and cloud-native architectures.

Job Types: Full-time, Permanent

Benefits:

• Flexible schedule
• Food provided
• Health insurance
• Leave encashment
• Paid sick time
• Paid time off
• Provident Fund
• Work from home

Work Location: In person