Data Engineer

Sago is hiring a Data Engineer to design, build, and maintain reliable data pipelines and analytics-ready datasets. The role focuses on data integration, transformation, quality controls, production troubleshooting, and collaboration with BI analysts, application teams, and business stakeholders.

Responsibilities:

• Design, develop, and maintain scalable ETL and ELT pipelines for batch and scheduled data processing
• Build and optimize data transformations using SQL, Python, and PySpark
• Ingest and integrate data from databases, APIs, files, SharePoint, and cloud storage
• Create and maintain curated, analytics-ready datasets for Power BI, Superset, and other reporting platforms
• Implement data-quality checks, reconciliation controls, monitoring, logging, and failure alerts
• Troubleshoot pipeline failures, schema mismatches, performance bottlenecks, and data discrepancies
• Optimize large-scale data processing for runtime, memory, storage, and cost efficiency
• Automate recurring workflows and schedule production jobs using orchestration tools such as Apache Airflow
• Maintain technical documentation, data mappings, business rules, and operational runbooks
• Collaborate with BI analysts, application teams, and business stakeholders to deliver dependable data solutions

Requirements:

• 2–5 years of experience
• 2–5 years of professional experience in data engineering, ETL development, or a similar role
• Strong SQL skills, including joins, common table expressions, window functions, data validation, and query optimization
• Hands-on experience with Python and/or PySpark for data processing and automation
• Practical experience building and supporting ETL or ELT pipelines in production
• Experience working with large datasets, incremental loads, upserts, schema changes, and partitioned data
• Good understanding of relational databases, dimensional modeling, data warehousing, and file formats such as Parquet, CSV, and JSON
• Experience with Git or another version-control system and structured deployment practices
• Strong debugging, analytical, documentation, and stakeholder-communication skills
• Experience with AWS services such as Amazon S3, Athena, EMR, and EMR Serverless
• Experience with Apache Spark, Delta Lake, Apache Airflow, Dremio, or Trino-compatible SQL engines
• Familiarity with Power BI, Apache Superset, or other downstream reporting tools
• Experience with REST APIs, SharePoint integrations, and automated file-delivery workflows
• Understanding of data security, access controls, privacy, and production monitoring practices