Type

Full-time

Experience

3+ yr

About this role

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: