Data Engineer (Mid-Level)

About Godaitec

At Godaitec, we are a full-service technology thought partner dedicated to empowering innovation. We blend creativity, strategy, and technology to transform ideas into scalable, future-ready solutions. From Software Development and Solution Consulting to cutting-edge Data Analytics and Emerging Technologies (AI, IoT, Blockchain), we help businesses turn their data into strategic value.

We are looking for a passionate Data Engineer to join our growing team. If you are driven by building robust data ecosystems and have a knack for solving complex data challenges, we want to hear from you.

Role Overview

We are seeking a skilled Data Engineer with up to 2 years of hands-on experience to build, optimize, and maintain our data infrastructure. You will play a critical role in our Data Analytics vertical, working on high-impact projects involving ETL pipelines, cloud data warehousing, and data governance. You will bridge the gap between raw data and actionable insights, ensuring our clients can make smarter, faster decisions.

Key Responsibilities

• Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines using industry-standard tools (Informatica, Talend, Spark) to ingest data from various sources.
• Data Modeling & Architecture: Develop robust data models and architectures that support business intelligence and advanced analytics requirements.
• Cloud Data Engineering: Implement and manage data solutions on cloud platforms (Databricks, Azure, AWS, or GCP).
• Data Quality & Governance: Establish frameworks for data quality, reliability, and governance to ensure data accuracy and compliance across the organization.
• Orchestration & Automation: Schedule and monitor workflows using orchestration tools like Airflow or Prefect to ensure timely data delivery.
• Containerization & CI/CD: Utilize Docker and Kubernetes for containerization and Jenkins for CI/CD pipelines to streamline deployment processes.
• Collaboration: Work closely with Data Scientists and Analysts to prepare data for visualization and machine learning models.

Candidate Profile & Requirements

1. Education & Experience

• Bachelor’s degree in computer science, Mathematics and statistics, or a related field.
• Up to 2 years of proven hands-on experience in data engineering.
• I must have prior experience working on Data Modeling, Data Operations, ETL pipelines, Data Quality, and Governance related projects.

2. Technical Proficiency

• ETL & Big Data: Strong proficiency in tools like Informatica or Talend. Experience with Big Data technologies such as Hadoop, Spark, and Kafka.
• Languages & Frameworks: Expert-level SQL and Python skills. Experience with Linux environments and PySpark for large-scale data processing.
• Cloud Platforms: Hands-on experience with Databricks and major cloud providers (Azure, AWS, or GCP).
• Orchestration: Proficiency in workflow management using Apache Airflow or Prefect.
• Containerization & CI/CD: Familiarity with Jenkins, Docker, and Kubernetes for deploying and scaling applications.
• Data Visualization: Ability to integrate data with visualization tools like Tableau or Power BI, and Python libraries like Matplotlib or Seaborn.

3. "Good to Have" (Bonus Skills)

• Generative AI & Agentic AI: Knowledge of or interest in GenAI applications and Agentic AI workflows is a significant plus as we expand into emerging technologies.
• Certifications: Any active Data Engineering certification from GCP, AWS, or Azure will be considered a strong advantage.

Why Join Godaitec?

• Work on Emerging Tech: Get the chance to work not just on traditional data stacks but also explore AI, IoT, and Blockchain integrations.
• Growth & Learning: We are a learning-centric organization that supports your professional development through exposure to diverse industries (Finance, Healthcare, Retail, etc.).
• Impact: Your work will directly influence the strategic decisions of global clients.

Job Type: Full-time

Education:

• Bachelor's (Preferred)

Location:

• Karachi (Preferred)

Work Location: In person