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Senior Data Platform Engineer (Microsoft Fabric)

Warsaw, Masovian Voivodeship, Poland

Pay
Pay not listed
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
Apply at Software Mind

What you’ll work on

Full posting
  • Design Bronze, canonical Silver and Gold Delta tables with PAN-safe physical layout

  • Build Fabric Spark notebooks for rolling velocity and merchant risk features

  • Build the canonical MERGE pipeline from Bronze to Silver, idempotent on _natural_key

  • Build Gold materialization pipelines for fact_transaction_risk and fact_merchant_risk

  • Design and implement Direct Lake semantic models and test their performance

From the employer’s posting
Design and implement Bronze, canonical Silver, and Gold Delta table schemas with PAN-safe physical layout
Build Fabric Spark notebooks for the feature pipeline (rolling velocity features across 24h / 7d / 30d windows, merchant-level rolling features, hierarchical rollups, cross-merchant features via pan_token joins).
Build the canonical MERGE DAG (Bronze → canonical Silver, idempotent on _natural_key per Aperia's canonical Fabric internal spec v1.4).
Build the Gold materialization pipeline for both grains (fact_transaction_risk, fact_merchant_risk)
Design and implement Direct Lake semantic models per reporting surface; performance-test against representative data volumes.

What you’ll bring

All qualifications

Core experience

  • 5+ years data engineering experience, with at least 2 years hands-on Spark development
  • Familiarity with published data-standard governance patterns — logical model vs physical profile, SCD2 dimensions, natural-key idempotency, extension attribute stores.
  • SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads.
  • Prior experience with fluid or schema-flexible ingestion patterns.
  • Strong Python data engineering skills — pandas, PyArrow, delta-rs.
  • Ability to communicate architecture clearly in writing and to work autonomously on well-scoped features.

Preferred experience

  • PySpark preferred
Qualification wording
5+ years data engineering experience, with at least 2 years hands-on Spark development (PySpark preferred).
Familiarity with published data-standard governance patterns — logical model vs physical profile, SCD2 dimensions, natural-key idempotency, extension attribute stores.
SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads.
Prior experience with fluid or schema-flexible ingestion patterns.
Strong Python data engineering skills — pandas, PyArrow, delta-rs.
Ability to communicate architecture clearly in writing and to work autonomously on well-scoped features.

Tools in this posting

  • Python
  • SQL
  • Azure
  • ClickHouse
  • Delta Lake
  • PostgreSQL
  • Power BI
  • Spark
  • Terraform
  • pandas
  • PySpark
Source — Tool mentions in context
- Configure OneLake namespace layouts, workspace catalogs, and cross-workspace shortcut relationships with the residuals programme. - Establish idempotent Delta write patterns using delta-rs from Python containers (segmented parallel writes with single atomic commit). - Fabric capacity sizing (F-SKU selection, load testing, Reservation vs PAYG decisions).
- SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads. - Strong Python data engineering skills — pandas, PyArrow, delta-rs. - Ability to communicate architecture clearly in writing and to work autonomously on well-scoped features.
- Parquet format expertise — footers, statistics allowlists, row-group sizing, dictionary encoding, column indexes. - Microsoft Fabric hands-on experience — workspaces, capacities, notebooks, lakehouses, semantic models, Direct Lake, SQL analytics endpoint. - SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads.
- Microsoft Fabric hands-on experience — workspaces, capacities, notebooks, lakehouses, semantic models, Direct Lake, SQL analytics endpoint. - SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads. - Strong Python data engineering skills — pandas, PyArrow, delta-rs.
- ClickHouse or similar OLAP engine familiarity. - Terraform for Azure data resources. - Prior experience with fluid or schema-flexible ingestion patterns.
- Partner with the DevOps engineer on Fabric IaC (Terraform capacity provisioning plus Fabric REST workspace bootstrap). - Consult on ClickHouse contingency if Direct Lake latency proves insufficient for client-tier UIs. Qualifications
- OneLake shortcuts and cross-workspace lakehouse patterns. - ClickHouse or similar OLAP engine familiarity. - Terraform for Azure data resources.
Project – the aim you’ll have Client is building a next-generation risk analysis and scoring platform on Microsoft Fabric, and this role owns its medallion data architecture end-to-end. You'll design and build Bronze / Silver / Gold Delta tables, Fabric Spark feature pipelines, Direct Lake semantic models, and physical-layout optimizations. This is our highest-utilization engineering role on the project — central to both Phase 1 platform foundation and Phase 2 risk product delivery, working alongside a Senior Technical Lead, Senior DevOps engineer, and mid-level developer. The platform ingests transaction and merchant data from clients via SFTP and API, tokenizes sensitive PAN data at the ingestion boundary using our reserved-BIN HMAC-derived format-preserving scheme, and lands data in canonical Silver on Microsoft Fabric per our internal canonical spec. You'll build the pipeline that populates canonical Silver via MERGE, consume it via OneLake shortcuts, and materialize risk-specific Gold tables that feed both custom-authored DMN rules and pre-scored Mastercard Brighterion feeds.
Role – how you’ll contribute - Design and implement Bronze, canonical Silver, and Gold Delta table schemas with PAN-safe physical layout — statistics allowlist configuration, liquid clustering, deliberate retention settings, V-Order optimization. - Build Fabric Spark notebooks for the feature pipeline (rolling velocity features across 24h / 7d / 30d windows, merchant-level rolling features, hierarchical rollups, cross-merchant features via pan_token joins).
- 5+ years data engineering experience, with at least 2 years hands-on Spark development (PySpark preferred). - Deep Delta Lake internals knowledge — transaction log format, checkpoints, retention settings (logRetentionDuration, deletedFileRetentionDuration), VACUUM, OPTIMIZE, deletion vectors, statistics collection. - Parquet format expertise — footers, statistics allowlists, row-group sizing, dictionary encoding, column indexes.
- Financial services or payments domain background. - Power BI semantic model authoring (DAX). - Familiarity with published data-standard governance patterns — logical model vs physical profile, SCD2 dimensions, natural-key idempotency, extension attribute stores.
Expectations – the experience you need - 5+ years data engineering experience, with at least 2 years hands-on Spark development (PySpark preferred). - Deep Delta Lake internals knowledge — transaction log format, checkpoints, retention settings (logRetentionDuration, deletedFileRetentionDuration), VACUUM, OPTIMIZE, deletion vectors, statistics collection.
- Fabric capacity sizing (F-SKU selection, load testing, Reservation vs PAYG decisions). - Partner with the DevOps engineer on Fabric IaC (Terraform capacity provisioning plus Fabric REST workspace bootstrap). - Consult on ClickHouse contingency if Direct Lake latency proves insufficient for client-tier UIs.

Benefits in the posting

Full benefits wording
  • Flexible and remote cooperation options
  • International projects with leading global clients
  • Travels related to international projects
  • Non-corporate atmosphere
  • Access* to private healthcare and life insurance
  • *The availability and terms of individual benefits may vary depending on the chosen form of cooperation.
  • Company Description

From the employer’s posting.

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Actively hiringFirst seen by us 13 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 17, 2026 · 13 days ago
Recorded sightings
31
Last seen by us
Oct 30, 2026

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Job description

View original posting ↗

Job Description

Project – the aim you’ll have

Client is building a next-generation risk analysis and scoring platform on Microsoft Fabric, and this role owns its medallion data architecture end-to-end. You'll design and build Bronze / Silver / Gold Delta tables, Fabric Spark feature pipelines, Direct Lake semantic models, and physical-layout optimizations. This is our highest-utilization engineering role on the project — central to both Phase 1 platform foundation and Phase 2 risk product delivery, working alongside a Senior Technical Lead, Senior DevOps engineer, and mid-level developer. 

The platform ingests transaction and merchant data from clients via SFTP and API, tokenizes sensitive PAN data at the ingestion boundary using our reserved-BIN HMAC-derived format-preserving scheme, and lands data in canonical Silver on Microsoft Fabric per our internal canonical spec. You'll build the pipeline that populates canonical Silver via MERGE, consume it via OneLake shortcuts, and materialize risk-specific Gold tables that feed both custom-authored DMN rules and pre-scored Mastercard Brighterion feeds.

Role – how you’ll contribute

  • Design and implement Bronze, canonical Silver, and Gold Delta table schemas with PAN-safe physical layout — statistics allowlist configuration, liquid clustering, deliberate retention settings, V-Order optimization. 
  • Build Fabric Spark notebooks for the feature pipeline (rolling velocity features across 24h / 7d / 30d windows, merchant-level rolling features, hierarchical rollups, cross-merchant features via pan_token joins). 
  • Author per-flavor-version Silver adapters — one function per flavor, unioned by name, tested against golden fixtures. 
  • Build the canonical MERGE DAG (Bronze → canonical Silver, idempotent on _natural_key per Aperia's canonical Fabric internal spec v1.4). 
  • Build the Gold materialization pipeline for both grains (fact_transaction_risk, fact_merchant_risk) with correct score-source stamping and idempotent merge semantics. 
  • Design and implement Direct Lake semantic models per reporting surface; performance-test against representative data volumes. 
  • Configure OneLake namespace layouts, workspace catalogs, and cross-workspace shortcut relationships with the residuals programme. 
  • Establish idempotent Delta write patterns using delta-rs from Python containers (segmented parallel writes with single atomic commit). 
  • Fabric capacity sizing (F-SKU selection, load testing, Reservation vs PAYG decisions). 
  • Partner with the DevOps engineer on Fabric IaC (Terraform capacity provisioning plus Fabric REST workspace bootstrap). 
  • Consult on ClickHouse contingency if Direct Lake latency proves insufficient for client-tier UIs. 

Qualifications

Expectations – the experience you need

  • 5+ years data engineering experience, with at least 2 years hands-on Spark development (PySpark preferred). 
  • Deep Delta Lake internals knowledge — transaction log format, checkpoints, retention settings (logRetentionDuration, deletedFileRetentionDuration), VACUUM, OPTIMIZE, deletion vectors, statistics collection. 
  • Parquet format expertise — footers, statistics allowlists, row-group sizing, dictionary encoding, column indexes. 
  • Microsoft Fabric hands-on experience — workspaces, capacities, notebooks, lakehouses, semantic models, Direct Lake, SQL analytics endpoint. 
  • SQL performance tuning skills for both OLAP (Fabric) and OLTP (PostgreSQL) workloads. 
  • Strong Python data engineering skills — pandas, PyArrow, delta-rs. 
  • Ability to communicate architecture clearly in writing and to work autonomously on well-scoped features. 

Additional skills – the edge you have

  • PCI-scoped data platform experience. 
  • Financial services or payments domain background. 
  • Power BI semantic model authoring (DAX). 
  • Familiarity with published data-standard governance patterns — logical model vs physical profile, SCD2 dimensions, natural-key idempotency, extension attribute stores. 
  • OneLake shortcuts and cross-workspace lakehouse patterns. 
  • ClickHouse or similar OLAP engine familiarity. 
  • Terraform for Azure data resources. 
  • Prior experience with fluid or schema-flexible ingestion patterns. 

Additional Information

What we offer:

  • Flexible and remote cooperation options
  • International projects with leading global clients
  • Travels related to international projects
  • Non-corporate atmosphere
  • Access* to language classes
  • Access* to knowledge-sharing initiatives
  • Access* to private healthcare and life insurance
  • Access* to multisport card

*The availability and terms of individual benefits may vary depending on the chosen form of cooperation.

Company Description

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines cooperation with enjoyment.

Source details

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Location & working pattern

Warsaw, Masovian Voivodeship, Poland

What we offer: - Flexible and remote cooperation options - International projects with leading global clients
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