Data Engineer
TLDR
Join a forward-thinking team using modern tools like Databricks and AWS to create scalable data pipelines and enable data-driven decision-making across the organization.
Data Engineer
Responsibilities
- Design and build scalable ETL/ELT pipelines using both batch and streaming approaches.
- Develop ingestion workflows from multiple sources such as databases, APIs, and event streams.
- Implement ingestion strategies including full load, incremental load, and CDC.
- Orchestrate data workflows using Apache Airflow.
- Manage data connectors using Airbyte.
- Work with Databricks Lakehouse to build and optimize data processing pipelines.
- Write and optimize complex SQL queries for analytics and transformation.
- Build modular and testable data models using dbt (staging → intermediate → marts).
- Maintain data quality, observability, and reliability across the platform.
- Work with AWS services such as S3, Lambda, EC2, IAM.
- Containerize data services using Docker and Kubernetes (EKS) when needed.
- Document pipelines, data models, and data dictionaries for long-term maintainability.
Requirements
- At least 5 years of experience in Data Engineering.
- Strong understanding of data architectures such as Data Lake, Data Warehouse, and Lakehouse.
- Hands-on experience with ETL/ELT pipelines, including batch and streaming processing.
- Familiar with ingestion patterns: full load, incremental, CDC, event-driven.
- Experience working with Databricks (Delta Live Tables, Jobs, Notebooks).
- Strong skills in PySpark or Spark SQL for large-scale data processing.
- Solid understanding of Delta Lake (ACID, time travel, schema evolution).
- Experience with Apache Airflow (DAGs, scheduling, monitoring).
- Experience with Airbyte or similar ingestion tools.
- Strong SQL skills (CTEs, joins, window functions, query optimization).
- Experience with dbt for transformation, testing, and documentation.
- Hands-on experience with AWS (S3, Lambda, IAM, etc.).
- Experience with Docker, Kubernetes (EKS).
- Experience running Airflow or Airbyte on Kubernetes.
- Familiar with data quality tools such as Great Expectations or Soda.
- Experience with Terraform or Infrastructure as Code.
- Exposure to data governance or catalog tools (e.g., Databricks Catalog).
- Experience with CI/CD pipelines (e.g., GitHub Actions).
- Strong Python skills for automation and pipeline scripting.
- Attractive salary range and we are open to negotiate if you're a strong fit.
- Hybrid/Remote-friendly culture, work where you grow best.
- Flexible hours, async teamwork (we respect your focus time).
- Work equipment support.
- Allowance for Certification & Skill Development.
- Year-end bonus & performance-based rewards.
- 15 paid leaves a year.
- Career growth with personal coaching sessions.
- Open, collaborative team culture - no micromanagement, only trust.
- Tools & AI-powered workflows that make remote work easier.
Benefits
Flexible Work Hours
Flexible hours, async teamwork (we respect your focus time).
Learning Budget
Allowance for Certification & Skill Development.
Personal coaching sessions
Career growth with personal coaching sessions.
Paid Time Off
15 paid leaves a year.
Remote-Friendly
Hybrid/Remote-friendly culture, work where you grow best.
Qode is a technology-driven platform that transforms how recruiters and candidates connect by leveraging data and automation. Our solutions streamline the hiring process through machine learning, creating private talent pools and automating workflows, ultimately enhancing the quality of candidate evaluation and decision-making. With our no-code tools, we empower organizations to develop tailored recruitment strategies without needing extensive technical skills.
- Industry
- Internet Software & Services