Clarity in decision making through Data and AI.
Big Data Engineer
Location
Greece
Posted
1 day ago
Salary
0
Seniority
Senior
Job Description
Big Data Engineer
Satori Analytics
**What Your Day Might Look Like:** - Design, build, and maintain data pipelines that serve models, data, and AI workflows to internal and client-facing applications. - Work across database types — relational, vector, and graph — modelling and storing data appropriately for each access pattern, in close collaboration with data scientists. - Build and maintain dbt models — writing transformation logic, tests, and documentation that ensure data quality and traceability end-to-end. - Operate what you build: instrument pipelines with logging, metrics, and tracing; diagnose and resolve production data issues before they become someone else's problem. - Write clean, tested, production-quality code and contribute to CI/CD pipelines and infrastructure-as-code. - Show up to code reviews, design discussions, and retrospectives — and have something worth saying.
Job Requirements
- Your Superpowers🚀:**
- 3–8 years of experience in data engineering, big data, or related roles.
- Strong SQL skills for querying, transformation, performance tuning, and data modeling
- Strong Python programming and scripting experience for ETL development, automation, and data processing.
- Hands-on experience with Apache Spark and PySpark for large-scale batch or streaming data processing
- Experience building and maintaining ETL/ELT pipelines in enterprise or on-premise environments.
- Good to have: Familiarity with Python backend API frameworks such as FastAPI, Flask, or Django for internal data services, orchestration endpoints, or platform integrations.
- Experience with relational databases and warehouse/lake platforms such as PostgreSQL, Hive, or enterprise data warehouses.
- Ability to implement data quality checks, testing, monitoring, and pipeline troubleshooting.
Benefits
- Perks on Perks:**
- Competitive salary and hybrid work model – come hang out in our Athens office or work remotely from anywhere in European economic Area (EU, Switzerland etc.) or UK (up to 6 weeks per year).
- Training budget to level up your skills from the top tech partners in the market (Microsoft, AWS, Salesforce, Databricks etc.) – whether it’s certifications or courses, we’ve got you covered.
- Private insurance, top-tier tech gear, and the chance to work with a stellar crew.
Related Guides
Related Categories
Related Job Pages
More Data Engineer Jobs
• Design and tune SQL and Snowflake models. • Build and orchestrate engineering pipelines that move data between systems on AWS. • Interrogate data and turn it into Power BI dashboards. • Use modern AI tooling to improve the platform.
• Lead the design, development, and optimization of scalable data platforms and pipelines. • Design, build, and maintain production-grade ETL/ELT workflows for batch and near real-time data processing. • Drive the migration and modernization of data assets from BigQuery and other analytical platforms into Snowflake. • Collaborate with business stakeholders, analysts, and engineering teams to translate business requirements into scalable data solutions. • Implement data quality, validation, monitoring, and observability frameworks.
• Reproduce a descriptive-statistics report end-to-end so any figure traces back to raw source — closing the gap the client admitted (numbers they can't currently defend). • Profile and reconcile differing source schemas across acquired entities: map differing field names, types, encodings and business definitions for the same concept into one conformed model. • Build dbt staging → intermediate → mart models with tests; codify the harmonized definitions the Data Science Lead specifies. • Write Great Expectations suites (null / range / uniqueness / referential checks) and wire them into the pipeline so bad data fails loudly rather than silently corrupting analysis. • Implement entity / identity resolution (deterministic + fuzzy matching) where there is no clean shared key for the same customer or account across sources. • Implement and verify anonymization / pseudonymization (hashing / tokenization / k-anonymity) and evidence that re-identification risk is controlled for the client's IT / compliance team. • Optimize Spark / Glue jobs over tens of millions of rows — partitioning, file formats (Parquet), incremental loads, cost control. • Orchestrate with Airflow / Step Functions; build repeatable, scheduled pipelines rather than one-off scripts. • Prepare clean, documented, feature-ready datasets for the PD / delinquency models. • Document runbooks so the offshore team can operate the pipelines and handover takes days, not weeks; help scope onboarding of the remaining (Ireland + additional) sources.
SAP Data Migration Consultant
KATBOTZ®Driving Customer Success Through Finance Transformation: Advanced Processes, Analytics, & AI.
• Define and execute end-to-end SAP data migration strategies. • Develop migration plans, timelines, and cutover activities. • Analyze legacy data structures and business requirements. • Define data mapping rules between source and target systems. • Identify data transformation and enrichment requirements. • Execute migration activities using SAP Migration Cockpit, SAP Data Services, LSMW. • Validate migrated data against business and technical requirements. • Provide hypercare and post-go-live support.
