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Location
Remote (Poland)
Seniority
Senior · 7+ years exp
Employment
Contractor

Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 2, 2026. Gravity Global scores A on the Alion truth index.

Overview
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Discover Gravity’s integrated marketing, branding, and media solutions—driven by data and creative excellence to fuel your growth. Accelerate your brand together.

gravity9 is a boutique IT consulting company headquartered in the UK with offices in the US, Canada, and Poland. Our team has deep experience in engineering, experience design and product management. We enjoy a challenge and pride ourselves on working with our clients on their most complex problems, finding elegant and flexible solutions that help them transform their businesses.

Role Description

We are seeking a highly motivated Senior Data Engineer to join our client's Product Development team within their Digital Practice. In this role, you will play a key part in client digital transformation journeys by delivering solutions that support decision-making, autonomous execution, and integrated operations. You will focus on productizing deep subject-matter expertise through robust data platforms and AI-enabled solutions built on Microsoft Azure. Operating within a flat, connected organization that values innovation and an entrepreneurial mindset, you will challenge existing ideas to take business outcomes to higher levels. The ideal candidate is a confident, passionate, and outcome-oriented leader with exceptional communication skills who excels at setting direction, mentoring team members, and leading directly by doing.

A successful candidate would:

· Work in Agile teams to deliver the committed backlog against acceptance criteria

· Design, build and operate ingestion and orchestration pipelines in Azure Data Factory

· Develop ETL/ELT in Azure Databricks using a medallion architecture to deliver trusted data for analytics, reporting and AI

· Lead code reviews and set engineering standards; take part in estimating, planning, testing, deployments and documentation

· Shape data architecture decisions, including metric definitions, semantic models and data that serves AI agents

· Apply data governance, security and access controls in line with client classification and residency requirements

· Use AI-assisted development tools responsibly to accelerate delivery without compromising quality or security

· Communicate progress, open items and issues to stakeholders, including management

· Stay abreast of relevant technology developments

Requirements

Experience:

· 7+ years’ experience in data engineering or software development, including 3+ years hands-on with Azure Databricks or Spark and actively mentoring junior team members. Proven capabilities in:

o Python and PySpark (expert level) and SQL

o Azure Data Factory, Azure Databricks (Delta Lake, Workflows, Unity Catalog), Azure Data Lake Storage and Azure SQL

o Data modelling (dimensional and lakehouse) and integration via APIs, OData, JDBC/ODBC and files

o Azure AI services (e.g. Azure AI Foundry, Azure OpenAI) and preparing data for AI agents

· Secure CI/CD and infrastructure-as-code (e.g. Terraform, Bicep, Databricks Asset Bundles) across DEV, UAT and PROD using Azure DevOps or GitHub, with sound Git branching practices

· AI-assisted development, with sound judgement in reviewing generated code

· Data quality, testing, security and privacy in regulated or restricted-data environments

· Designing end-to-end data solutions and recommending new frameworks and technologies

· Agile and Kanban delivery in mixed-discipline teams, building consensus across subject matter experts, engineers, data scientists and designers

· A self-starter who is organized and needs minimal supervision

· Bachelor’s degree in computer science, Engineering or a related field, or equivalent education and experience

Nice to have:

· Experience in integrating enterprise systems (e.g. ERP, project management or document management platforms)

· Data management and governance concepts (e.g. data quality, cataloguing, lineage, master data, stewardship) using any supporting toolset

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