Senior Data Engineer (AWS / Databricks)

Department: Analytics & Data

Location: Europe (remote)

Employment Type: FullTime

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 advantages

• Experience with Terraform or another Infrastructure as Code tool

• Hands-on experience with Unity Catalog

• Experience with Databricks Jobs, Workflows, or Lakeflow

• Understanding of AWS networking, VPCs, and EKS environments

• Experience with CDC technologies such as AWS DMS or Debezium

• Experience working with subscription and payments data

• Familiarity with Stripe, Adyen, Solidgate, or other payment service providers

• Experience integrating marketing or attribution data

• Experience with dbt

• Previous responsibility for defining data-platform standards or reusable engineering patterns

• Experience building a data platform in a startup, scale-up, or other ambiguous environment

What success could look like

During your first stage in the role, you will help:

• Establish the core AWS, Databricks, S3, Unity Catalog, and Terraform platform foundation

• Productionize the first reusable end-to-end ingestion pattern

• Onboard and unify payments data across multiple products

• Deliver core Gold models for revenue, subscriptions, churn, and LTV

• Create and document a repeatable approach for onboarding additional products

• Put monitoring, reconciliation, CI/CD, and cost controls into production

What our client offers

• Competitive compensation

• Fully remote work with flexible working hours

• 22 paid vacation days plus local public holidays

• A modern engineering environment with contemporary technologies

• The opportunity to shape a growing Data function and its technical foundations

• Meaningful platform challenges with room to influence architecture and engineering practices

• A collaborative, product-focused environment where data directly supports business decision