Mid Data Engineer

We are:
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.With the right people and the right ideas, there’s no limit to what we can achieve

Are you a fit?

Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

Responsibilities:

Existing platform (Databricks)

• Keep production pipelines running: ingestion, transformation, and delivery to downstream consumers.
• Diagnose and resolve pipeline failures and data quality issues, often without documentation to fall back on.
• Reverse-engineer and document existing transformation logic and business rules — this is the input the migration depends on.
• Migrate legacy tables from Hive Metastore to Unity Catalog.
• Maintain Iceberg-enabled table sharing between Databricks and Snowflake.

New development (Snowflake, dbt, Airflow)

• Build and test dbt models, including incremental materializations and data tests.
• Develop and maintain Airflow DAGs for orchestration.
• Validate that migrated pipelines produce output equivalent to the Databricks versions.
• Contribute to Snowflake modeling, performance, and cost decisions.

Across both

• Work directly with client stakeholders on technical topics, alongside the team lead.

Technical Requirements

Databricks

• PySpark and SQL — able to read, debug, and modify existing pipelines. Deep Spark tuning is not required.
• Delta Lake: MERGE/upsert patterns, table properties, OPTIMIZE, partitioning.
• Databricks Workflows, cluster configuration, job troubleshooting.
• Unity Catalog: catalogs, schemas, grants, lineage, and the metastore model.

Snowflake

• Warehouses, roles and grants, and the general operating model.
• Query performance and an awareness of how compute cost behaves.

Dbt

• Models, sources, tests, and incremental materializations.
• Project structure and how dbt fits into a deployment workflow.

Airflow

• Writing and maintaining DAGs, operators, scheduling, and dependency management.
• Understanding retries, backfills, and idempotent task design.

Fundamentals

• 3+ years operating production data pipelines.
• Strong SQL — window functions, complex joins, reading transformation logic written by someone else.
• Python for scripting, automation, and API integration.
• Incremental loading patterns, idempotency, late-arriving data, reprocessing.
• AWS: S3, IAM basics. Basic working knowledge of Redshift and its role in the wider architecture.

Ways of working

• Fluent English — client-facing role with stakeholders based abroad.
• Self-directed. Able to make progress on an unfamiliar codebase without a structured onboarding path, and comfortable asking good questions when context is missing.
• Clear communicator: can explain a production incident to a non-technical stakeholder and give a realistic ETA.

Nice-to-have:

• Experience with an actual platform migration, not only greenfield work.
• Open table formats, particularly Iceberg and cross-platform sharing.
• Clickstream or web analytics data (Adobe Analytics, Google Analytics, Segment).
• Experience taking over an undocumented system and stabilizing it.
• AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows. 

What we offer:

• A High-Impact Environment
• Commitment to Professional Development
• Flexible and Collaborative Culture
• Global Opportunities
• Vibrant Community
• Total Rewards

*Specific benefits are determined by the employment type and location.

Find out more about our culture here.