2 Π΄Π½Ρ Π½Π°Π·Π°Π΄
Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Vice President (AI)
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
Π’Π΅ΠΊΡΡ:
TL;DR
Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Vice President (AI): Building production data pipelines, curated datasets, and platform capabilities for analytics and AI use cases with an accent on distributed processing, data modelling, and data quality. Focus on designing scalable Lakehouse workflows, reconciling historical data, optimizing performance, and leading delivery across engineering workstreams.
Location: Stockholm, Sweden
Company
is a global investment banking, securities and investment management firm founded in 1869, with offices around the world.
What you will do
- Build, enhance, and support batch and streaming data pipelines on the Lakehouse and AI data platform.
- Develop raw, refined, and curated datasets for analytics, reporting, and AI use cases.
- Apply data modelling, schema evolution, partitioning, clustering, and performance optimization techniques.
- Implement data quality controls, reconciliation processes, monitoring, testing, and root-cause analysis.
- Collaborate with engineers, platform teams, and data consumers to deliver production-ready data products.
- Lead technical design and delivery for workstreams while supporting less experienced engineers.
Requirements
- Bachelorβs or masterβs degree in a relevant discipline, or equivalent practical experience, with strong quantitative or data engineering expertise.
- Strong hands-on programming experience in Python or Java and good working knowledge of SQL.
- Experience building or supporting production data pipelines in a collaborative engineering environment.
- Experience with distributed data processing frameworks such as Apache Spark.
- Knowledge of software engineering fundamentals, including version control, testing, release discipline, and CI/CD.
- Understanding of temporal data modelling, schema design, data compatibility, data quality, reconciliation, and scalable performance techniques.
Nice to have
- Experience with Kafka, Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, or Sybase IQ.
- Experience with JSON, Avro, and Parquet data formats.
- Experience with containerized or Kubernetes-based deployment approaches.
Culture & Benefits
- Opportunities for professional and personal development through training and development programs.
- Firmwide networks, wellness programs, personal finance offerings, and mindfulness programs.
- Commitment to diversity, inclusion, and reasonable accommodations during the recruiting process.
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β
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