Data Engineer

We’re looking for a Data Engineer who can build, scale, and maintain robust data pipelines and infrastructure. You’ll work closely with data analysts, data scientists, and software engineers to ensure reliable data flow, storage, and accessibility across the organization.

If you enjoy turning messy data into clean, usable systems and care about performance, scalability, and reliability—this role is for you.

Key Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines

Develop and optimize data architectures (data lakes, warehouses)

Ensure data quality, integrity, and consistency across systems

Collaborate with cross-functional teams to understand data needs

Monitor and troubleshoot data pipeline performance issues

Implement data security and governance best practices

Work with large datasets in batch and real-time processing systems

Automate data workflows and improve efficiency

Document data systems and processes clearly

Required Skills Technical Skills

Strong proficiency in Python and/or Java/Scala

Solid experience with SQL (advanced querying, optimization)

Hands-on experience with ETL tools (e.g., Apache Airflow, Talend)

Experience with big data frameworks like Apache Spark or Hadoop

Familiarity with data warehousing solutions (e.g., Snowflake, Amazon Redshift, Google BigQuery)

Knowledge of cloud platforms (AWS, GCP, or Azure)

Experience with data modeling (star schema, snowflake schema)

Understanding of APIs and data integration techniques

Version control using Git

Preferred / Bonus Skills

Experience with real-time streaming (e.g., Apache Kafka, Flink)

Containerization tools like Docker and orchestration with Kubernetes

CI/CD pipeline setup and automation

Familiarity with data governance, compliance, and security standards

Exposure to machine learning pipelines or MLOps

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

2+ years of experience in data engineering or related role