We are looking for a Senior Data Engineer with 4 to 5 years of experience to build data pipelines for an enterprise finance data platform on Azure, Databricks and Snowflake.
Responsibilities
Build ingestion pipelines from finance source systems using Azure Data Factory, change data capture, and API and event-driven feeds
Develop transformations in Databricks across the Bronze, Silver and Gold layers using PySpark and Spark SQL
Build conformed dimensions and fact tables to the agreed data model
Load certified Gold data into Snowflake and build the views used by reporting teams
Add data quality, reconciliation and logging checks to pipelines
We are looking for an experienced Data Engineer to join a project focused on building and developing modern cloud-based data solutions using Microsoft Azure, Azure Data Factory, and Snowflake.
Lead discovery and requirements gathering, translate business needs into actionable requirements, investigate and analyse data, connect business processes and technology, challenge assumptions and shape solutions, turn analysis into insights, support end-to-end delivery, drive quality and readiness, manage risks and dependencies, build team capability.
Responsibilities
Lead discovery and requirements gathering
Translate business needs into actionable requirements
Investigate and analyse data
Connect business processes and technology
Challenge assumptions and shape solutions
Requirements
Relevant tertiary qualification in business analysis, data management, information technology or a related discipline, or equivalent professional experience
Turns complex commercial, distributor, pricing, consumer and market data into scalable Power BI solutions to enable RMG and Sales teams to make fact-based decisions.
Responsibilities
Optimize existing reporting
Build new analytical products and semantic models
Enable RMG and Sales teams to make faster, fact-based decisions
Develop Power BI solutions
Communicate analytical findings clearly to technical and non-technical stakeholders
Lead a team of data engineers building ingestion and transformation pipelines for an enterprise finance data platform on Azure, Databricks and Snowflake.
Responsibilities
Lead a team of data engineers building ingestion and transformation pipelines
Deliver ingestion from finance source systems using change data capture, Azure Data Factory batch loads, API and event-driven feeds, and governed manual uploads
Build the Bronze, Silver and Gold layers in Databricks, including conformed dimensions and Gold data domains
Build Gold data domains for financial control, FP&A, tax, recharges and project accounting with the Data Architect
Publish certified data products from Databricks to Snowflake for reporting and analysis
Lead and evolve the Microsoft Fabric and Power BI platform, ensuring reliability, scalability, and governance while delivering data products and insights to senior leadership.
Responsibilities
Own and evolve the Microsoft Fabric platform: capacity sizing, performance, scalability, availability and cost control
Maintain and extend metadata-driven pipelines integrating Salesforce/Kantata, Dynamics 365, Workday and other sources
Develop and maintain medallion lakehouses/warehouses
Design and deliver semantic models and Power BI reports
Run Power BI governance: workspaces, access control, release management, app embedding and user adoption
Generous referral bonuses, offering up to PLN6,000 for referring specialists. Additional revenue sharing opportunities for initiating partnerships with new clients.
Responsibilities
Design, build and maintain batch and incremental ETL/ELT pipelines from source systems to Snowflake.
Develop and orchestrate data pipelines using Azure Data Factory, including linked services, datasets, data flows, triggers and reusable parameterised pipelines.
Design and maintain data models in Snowflake, covering staging, core and data mart layers.
Manage Snowflake warehouses, schemas, roles and data-loading mechanisms such as COPY INTO, Snowpipe and stages.
Write, optimise and maintain complex SQL queries for data transformations, reporting and data quality validation.
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