Data Engineer

📋 Description

• Design, construct, and sustain scalable AWS data platforms that support both batch and streaming pipelines, analytics, and AI/ML workloads, in accordance with AWS Well-Architected best practices.

• Develop and manage data ingestion, transformation, and enrichment pipelines from both internal systems and external APIs, accommodating structured, semi-structured, unstructured, and graph data.

• Execute data normalization workflows to guarantee consistent schemas, high data quality, and dependable analytics, BI, and ML applications.

• Create and enforce data governance policies that include cataloging, lineage, access control, and auditability.

• Construct and manage knowledge graphs that model relationships among core business entities, facilitating advanced analytics and inference.

• Detect data gaps, inconsistencies, and absent relationships through strong analytical and inference capabilities.

• Integrate data from enterprise systems such as CRM and ERP platforms (Salesforce, HubSpot, SAP, NetSuite, Dynamics 365, Workday).

• Design secure data access layers for analytics, BI, ML, and subsequent applications.

• Implement monitoring, observability, and data quality checks to ensure freshness, completeness, and the health of data pipelines.

• Enhance data architectures for performance and cost-effectiveness by utilizing partitioning, indexing, compression, and storage tiering strategies.

• Develop internal tools, dashboards, and standardized scaffolding to boost visibility, maintainability, and facilitate onboarding.

• Collaborate with cross-functional teams to deliver impactful data solutions while sharing best practices, documentation, and technical guidance.

⛳️ Requirements

• Extensive experience in designing and operating AWS data platforms, including S3, Glue, Lake Formation, Athena, Redshift, EMR, Kinesis/MSK, DynamoDB, OpenSearch, and Neptune.

• Proficient Python programming skills for data engineering, emphasizing modular, testable, and maintainable code.

• Strong grasp of distributed data systems, encompassing batch and streaming pipelines, fault tolerance, idempotency, and event-driven architectures.

• Experience with data warehouse and lakehouse architectures, ETL/ELT pipelines, and analytical query engines.

• Practical experience with Spark, Hadoop, Hive, or Flink.

• Robust data modeling abilities, including normalized, denormalized, and graph-based models, with safe schema evolution practices.

• Advanced SQL competencies for analytics and data engineering, featuring window functions, CTEs, and query optimization techniques.

• Experience in integrating external APIs and enterprise systems, particularly CRM and ERP platforms.

• Familiarity with data governance, security, and compliance, including encryption, access control, and audit logging practices.

• Experience with implementing monitoring, observability, and data quality checks utilizing CloudWatch and CloudTrail.

• Proficiency in Infrastructure as Code using CloudFormation or Terraform.

• Strong end-to-end ownership mindset, prioritizing scalability, reliability, and long-term maintainability.

• Professional-level English communication skills, capable of articulating data architectures and trade-offs to both technical and non-technical audiences.

🏝️ Benefits

• Remote-First Flexibility: Experience work-life balance in a remote-first setting that enables you to work from anywhere.

• Innovative Culture: We adopt a startup mindset, fostering creativity, agility, and growth.

• Career Development: Avahi is dedicated to your professional growth, providing mentorship and opportunities for career advancement.

• Purpose-Driven Mission: Join us in making a positive impact. Avahi is committed to promoting diversity, supporting women in tech, and encouraging sustainable practices.

• Global Collaboration: Collaborate with a diverse, skilled team, exchanging insights and working together to create innovative solutions that truly matter.