Lead Data Engineer / Data Platform Architect (AWS) at Descasio

Artificial Intelligence
February 18, 2026
Full Time
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Job Description

Role Summary

Descasio is seeking a hands-on Lead Data Engineer / Data Platform Architect to design, build, secure, and operate an enterprise-grade AWS-native DataHub.

This platform will serve two purposes:

  • Power Descasio’s internal analytics and decision-making.
  • Evolve into a commercial, multi-tenant DataHub offering for enterprise customers.

This is a capability-defining role, not a support role. You will own architecture decisions end-to-end, mentor junior engineers, and help turn a data platform into a repeatable, revenue-generating service aligned with Descasio’s AWS strategic partnership ambitions.

Ideal Candidate Profile

You are:

  • Deeply Technical & Commercially Aware: You are a hands-on senior data engineer who can architect, build, secure, and operate an enterprise-grade data platform end-to- end, but you are also pragmatic and business-aware.
  • An Owner: You are comfortable owning architecture decisions end-to-end and can work in a lean environment without heavy supervision.
  • Biased for Action & Quality: You have a strong bias for automation, security-by-design, and scalability.
    A Mentor: You enjoy teaching, documenting, and up-skilling others.
  • A Product Builder: You are interested in building products, not just pipelines, and are comfortable collaborating with cloud, security, and sales teams.

Experience:

  • 7‒10+ years in data engineering or data platform roles.
  • 4+ years designing and running production data platforms.
  • Strong, hands-on AWS experience (mandatory).
  • Prior exposure to multi-tenant SaaS or managed services is a strong plus.

Key ResponsibilitiesData Platform Architecture & Delivery:

  • Design and implement an enterprise-grade data architecture covering data ingestion (batch & near-real-time), a central data lake, a curated/analytics-ready data warehouse, and a semantic/consumption layer.
  • Lead the build of Descasio’s internal pilot data platform using real enterprise datasets.
  • Ensure the platform is scalable, secure, cost-efficient, and cloud-native.

AWS-First Implementation:

  • Own the selection and implementation of AWS services such as Amazon S3, AWS Glue / Lake Formation, Amazon Redshift / Athena, AWS IAM, KMS, VPC, Step Functions / MWAA (Airflow), CloudWatch, and CloudTrail.
  • Define and implement best practices for data partitioning & formats (Parquet, Iceberg, Delta), performance optimization, and cost controls.
  • Design the platform in alignment with the AWS Well-Architected Framework.
  • Implement FinOps principles: cost allocation, tagging, budgets, and guardrails.
  • Produce delivery artifacts usable for AWS partner validations and references.

Multi-Tenancy & Security Design:

  • Architect a multi-tenant data platform that supports strong tenant isolation (data, compute, metadata), fine-grained access control, encryption at rest and in transit, and auditability and compliance readiness.
  • Design repeatable tenant onboarding, off-boarding, and isolation patterns.
  • Work closely with Descasio’s Security Practice to align with security offerings.

Data Governance & Quality:

  • Define and implement data governance models, data quality checks, metadata management, and lineage and cataloging.
  • Establish clear standards for naming, versioning, and schema evolution.

Analytics & AI Readiness:

  • Ensure the platform supports BI tools (Power BI, Looker, etc.) and advanced analytics and ML workloads.
  • Design data models optimized for business decision-making.
  • Enable future AI/ML and GenAI use cases without over-engineering early AI pipelines.

Mentorship & Capability Building:

  • Mentor and upskill existing data analysts and junior engineers.
  • Provide hands-on guidance, code reviews, and architecture walkthroughs.
  • Create reusable templates, IaC modules, playbooks, and documentation.
  • Help Descasio grow an internal Data Engineering capability.

Productization & Customer Readiness:

  • Help define how the platform is packaged as a paid managed service, including service tiers, SLAs, and reference architectures.
  • Contribute to service descriptions and customer onboarding models.
  • Support pre-sales discussions, demos, and solution assurance where deep technical input is required.

Required Skills & CompetenciesTechnical (Must-Have):

  • Strong SQL and data modeling skills.
  • Advanced, production-grade AWS experience.
  • Data lake and data warehouse architecture design.
  • ETL/ELT pipeline development.
  • Infrastructure-as-Code (Terraform / CloudFormation).
  • Python for data engineering.
  • A security-first and cost-aware mindset.
  • Familiarity with the AWS Well-Architected Framework.

Nice-to-Have:

  • Experience with modern data formats like Apache Iceberg, Delta Lake, or Hudi.
  • Exposure to dbt.
  • SaaS or managed services background.
  • Experience supporting regulated industries (e.g., Finance, Oil & Gas).
  • AWS partner competency or validation experience.
  • Experience with multi-account AWS strategies.
  • Exposure to pre-sales or customer solution design.