Data Engineer

Contango - a wholly owned subsidiary of ADQ, operates as a strategic consultancy focused on retaining value within the ADQ's ecosystem of 40+ portfolio companies

Tasks

About the Role

We are an emerging AI-native product-driven, agile start-up under Abu Dhabi government AND we are seeking a motivated and technically versatile Data Engineer to join our team. You will play a key role in delivering data platforms, pipelines, and ML enablement within a Databricks on Azure environment.

As part of a stream-aligned delivery team, you’ll work closely with Data Scientists, Architects, and Product Managers to build scalable, high-quality data solutions for clients. You'll be empowered by a collaborative environment that values continuous learning, Agile best practices, and technical excellence.

Ideal candidates have strong hands-on experience in Databricks, Python, ADF and are comfortable in fast-paced, client-facing consulting engagements.

Skills and Experience requirements
1. Technical

• Databricks (or similar) e.g. Notebooks (Python, SQL), Delta Lake, job scheduling, clusters, and workspace management, Unity Catalog, access control awareness
• Cloud data engineering – ideally Azure, including storage (e.g., ADLS, S3, ADLS), compute, and secrets management
• Development languages such as Python, SQL, C#, javascript etc. especially data ingestion, cleaning, and transformation
• ETL / ELT – including structured logging, error handling, reprocessing strategies, APIs, flat files, databases, message queues, event streaming, event sourcing etc.
• Automated testing (ideally TDD), pairing/mobbing. Trunk Based Development, Continuous Deployment and Infrastructure-as-Code (Terraform)
• Git and CI/CD for notebooks, data pipelines, and deployments

2. Integration & Data Handling

• Experienced in delivering platforms for clients – including file transfer, APIS (REST etc.), SQL/NoSQL/graph databases, JSON, CSV, XML, Parquet etc
• Data validation and profiling - assess incoming data quality. Cope with schema drift, deduplication, and reconciliation
• Testing and monitoring pipelines: Unit tests for transformations, data checks, and pipeline observability

3. Working Style

• Comfortable leveraging the best of lean, agile and waterfall approaches. Can contribute to planning, estimation, and documentation, but also collaborative daily re-prioritisation
• Able to explain technical decisions to teammates or clients
• Documents decisions and keeps stakeholders informed
• Comfortable seeking support from other teams for Product, Databricks, Data architecture
• Happy to collaborate with Data Science team on complex subsystems

Requirements

Nice-to-haves

• MLflow or light MLOps experience (for the data science touchpoints)
• Dbt / dagster / airflow or similar transformation tools
• Understanding of security and compliance (esp. around client data)
• Past experience in consulting or client-facing roles

Candidate Requirements

• 5–8 years (minimum 3–4 years hands-on with cloud/data engineering, 1–2 years in Databricks/Azure, and team/project leadership exposure)
• Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Data Engineering

Job Type: Full-time

Benefits

Visa, Insurance, Yearly Flight Ticket, Bonus scheme, relocation logistics covered

Interviewing process consists of 2 or 3 technical/behavioral interviews