Data Engineer - KIS Solutions

This is a remote position.

We are looking for Data Engineers (Junior, Mid or Senior)!

• Design, build, and maintain end-to-end data pipelines (batch and/or streaming), from ingestion to transformation and delivery.
• Develop and operate ETL/ELT workflows, ensuring reliability, scalability, and performance.
• Write efficient, production-grade SQL queries for data extraction, transformation, and analytics use cases.
• Implement and maintain data models (e.g., star schemas, incremental models) optimized for analytics and reporting.
• Develop reusable and modular Python code for data transformations and pipeline logic.
• Monitor data pipelines, troubleshoot failures, and perform root cause analysis across code, orchestration, data sources, and cloud services.
• Ensure data quality by implementing automated validation checks (schema validation, freshness checks, row-level assertions).
• Translate business and analytical requirements into robust technical data solutions.
• Collaborate with analysts, backend engineers, and other stakeholders to define data contracts and ensure data availability.
• Actively participate in planning, estimation, and prioritization of data engineering tasks.
• Proactively identify risks related to performance, scalability, or data integrity and propose mitigation strategies.
• Contribute to continuous improvement of data platforms, processes, and team practices.
• Write and maintain technical documentation for pipelines, schemas, and data lineage.
• Communicate clearly with team members and clients, raising questions and concerns when requirements or priorities are unclear.
• Support and mentor other team members when appropriate, contributing to overall team delivery.

Requirements

• Professional experience as a Data Engineer working with production data pipelines.
• Strong experience with SQL, including query optimization, indexing, partitioning, and performance trade-offs.
• Professional experience writing Python for data transformations, following good design and modularization practices.
• Experience designing and implementing data models for analytics use cases.
• Experience building and operating pipelines using cloud-based data platforms.
• Hands-on experience with Azure, Databricks, and Data Lake environments.
• Experience operating data pipelines, including error handling, monitoring, and data quality processes.
• Familiarity with Git for version control, including branching and resolving merge conflicts.
• Experience working with Kubernetes or containerized data workloads.
• Understanding of data formats such as Parquet or ORC, including cost and performance considerations.
• Knowledge of basic data security and governance practices (access control, masking, PII handling).
• Ability to deliver l