Senior Data Platform Engineer

Aperia•Dallas, TX

8d•Hybrid

About The Position

Aperia Solutions 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 Aperia's 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.

Requirements

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.
Must be willing to submit to a background investigation and drug test as part of the selection process.

Nice To Haves

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.

Responsibilities

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.