Data Engineer And Operations Specialist

Siemens Energy; Lisboa; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

Siemens Energy in Portugal is seeking a Data Engineer and Operations Specialist to own HR master data integrity across our landscape. Your day blends hands-on analysis (SQL/Python), ingestion quality work on our AWS/Snowflake-based HR Data Hub, and collaboration with domain and system owners to diagnose breaks between source systems (Workday, local HCMs), hub, and downstream consumers.

Your goal is clean, timely, and trusted HR data, validated at entry rather than repaired downstream, with a

Qualifications

• Proficiency in SQL and Python.
• Experience with ETL/orchestration and REST/batch APIs.
• Hands-on Snowflake on AWS with S3 ingestion patterns.
• Knowledge of HR master data domains and privacy requirements.
• Experience in data profiling, matching and deduplication.
• Familiarity with Workday or SAP HCM/S4 is a plus.

Responsibilities

• Own ingestion quality gates and automated validation at entry for inbound integrations.
• Ensure end-to-end data quality across source, hub and target systems.
• Develop matching and reconciliation for person, position and org structures.
• Build reliable Snowflake/Snowpipe pipelines with monitoring and SLAs.
• Maintain interfaces (Workday provider; local HR data hub) and support changes.
• Plan and execute UAT/regression testing and track defects.

Skills

SQL

Python

ETL

APIs

Tools

Snowflake

Snowpipe

AWS

S3

dbt

CloudWatch

Great Expectations

Job description

Data Engineer and Operations Specialist

About the Role

Job ID 287350 Location Portugal Lisboa Amadora Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy Unipessoal Lda. Organization EVP Global Functions Business Unit Digital Core Full / Part time Full-time Experience Level Professional / Experienced

A Snapshot of Your Day

You will own the integrity of HR master data across our landscape. Your day blends hands‑on analysis (SQL/Python), ingestion quality work on our AWS/Snowflake‑based HR Data Hub, and collaboration with domain and system owners to diagnose breaks between source systems (Workday, local HCMs), the hub, and 235+ downstream consumers. Your goal: clean, timely, and trusted HR data, validated at the point of entry, not repaired downstream.

How You’ll Make an Impact

• Ingestion quality gates: Design and operate automated validation at the point of entry for inbound integrations, schema checks, completeness, and referential integrity against source; so downstream consumers receive trusted data by default.
• End-to-end data quality assurance: Ensure correct HR master data across source, hub, and target systems; identify, classify, and resolve data errors with documented root cause.
• Data matching & reconciliation: Build matching logic and reconciliation processes (person, position, org structures) between source systems and the hub to enforce consistency.
• Pipeline reliability & monitoring: Develop and optimize Snowflake/Snowpipe ingestion and integration pipelines, including monitoring, alerting, and SLAs.
• Interface stewardship: Maintain technical interfaces (Workday data provider; local HCM HR Data Hub receiving systems) and support change delivery.
• Test support for enhancements: Plan and execute UAT/regression tests; track defects and validate production fixes.

What You Bring

• Data engineering skills: Proficiency in SQL and Python; experience with ETL/orchestration and REST/batch APIs.
• Cloud data platform experience: Hands‑on with Snowflake (Snowpipe, Streams/Tasks) on AWS; understanding of S3‑based ingestion patterns, IAM least‑privilege access, and CloudWatch monitoring.
• Data quality tooling: Experience implementing automated DQ checks at ingestion (, Great Expectations, dbt tests, Snowflake data metric functions).
• HR data domain expertise: Knowledge of HR master data domains (employee, position, organization, compensation) and lifecycle events; hands‑on experience with Workday or SAP HCM/S4 is a plus.
• Quality mindset: Experience in data profiling, matching, deduplication, reconciliation, and root cause analysis.
• Compliance: Understanding of privacy, access control, and audit requirements in HR data (regional requirements apply).