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