Senior Data Engineer (AWS / Databricks)

Remote (EU/Ukraine) | Full-time

We’re hiring on behalf of our client — an international product company building and scaling a portfolio of subscription-based digital products for global markets.

The company is now building a centralized data platform that will bring together fragmented product, payments, marketing, and operational data across its portfolio. They are looking for a hands-on Senior Data Engineer to help establish Databricks on AWS, define the platform’s core engineering standards, and build reliable data products for analysts and business stakeholders.

This is a greenfield platform role with substantial technical ownership. You will not be joining a mature data environment with established patterns. You will help design those patterns, make foundational technical decisions, and create a repeatable approach for onboarding new products and data sources.

Why this role is interesting

• Build a centralized data platform from an early stage rather than inherit a mature warehouse
• Influence architecture, engineering standards, ingestion patterns, and governance
• Solve a complex platform challenge involving distributed PostgreSQL databases across private AWS and EKS environments
• Build the first portfolio-wide data models around payments, subscriptions, revenue, churn, LTV, and CAC
• Work closely with Data, DevOps, Product, Backend Engineering, and business stakeholders
• See a direct connection between your engineering work and key product and commercial decisions

What you’ll do

Build the Data Platform

• Build and operate a Databricks-based data platform on AWS together with the Data and DevOps teams
• Design and maintain Bronze, Silver, and Gold data layers using S3 and Delta Lake
• Develop reusable ingestion patterns for PostgreSQL databases, S3, APIs, webhooks, and SaaS platforms
• Build and manage production workflows using Databricks Jobs and Workflows
• Contribute infrastructure changes through Terraform, Git, and pull-request-based workflows
• Help establish platform standards, development patterns, and technical documentation

Build Reliable Data Pipelines

• Implement incremental data loads, historical backfills, idempotent reprocessing, and schema-change handling
• Design safe ingestion from multiple production PostgreSQL databases without creating unnecessary risk or load for source applications
• Handle late-arriving updates, deletes, retries, and pipeline recovery
• Build monitoring, freshness checks, reconciliation processes, and data-quality controls
• Troubleshoot pipeline failures and data inconsistencies across multiple products and source systems
• Optimize Databricks compute, SQL workloads, and storage for performance, reliability, and cost

Unify Product and Payments Data

• Standardize fragmented product and payments data across the company’s portfolio
• Build common analytical entities for users, subscriptions, transactions, renewals, refunds, and chargebacks
• Normalize product-specific schemas into reliable source-of-truth models
• Deliver trusted Gold datasets and data marts for Payments, Marketing, Product, Finance, and executive reporting
• Support analytical use cases related to revenue, subscriptions, churn, LTV, CAC, product funnels, and attribution

Establish Governance and Engineering Standards

• Contribute to Unity Catalog implementation and ongoing governance
• Help manage groups, permissions, service principals, and data access patterns
• Apply Git-based development, code review, CI/CD, testing, and documentation practices
• Work with Product and Backend teams to understand source tables, relationships, and business logic
• Help define repeatable patterns for onboarding new products and data sources

Target Platform

• AWS
• Databricks
• Spark / PySpark
• S3
• Delta Lake
• Unity Catalog
• Databricks Jobs / Workflows
• PostgreSQL
• Python
• SQL
• Terraform
• Git and CI/CD

What we’re looking for

• Strong production experience in Data Engineering
• Advanced Python and SQL skills
• Hands-on production experience with Databricks and Spark/PySpark
• Practical AWS experience, particularly with S3 and IAM
• Experience ingesting data from PostgreSQL or other relational databases
• Strong understanding of incremental pipelines, historical backfills, idempotency, retries, and reprocessing
• Experience designing analytical data models and working with medallion architecture
• Experience implementing data-quality checks, monitoring, reconciliation, and troubleshooting
• Experience with Git-based development and CI/CD workflows
• Ability to take ownership of complex data initiatives from design through production operation
• Comfort working in a greenfield environment where standards, ingestion patterns, and models are still being defined
• Ability to collaborate effectively with DevOps, Backend Engineering, Product, Analytics, and business stakeholders

Strong …