Lead Data Engineer (Databricks)

Acaisoft; Warszawa; On-site; PLN 180,000 - 240,000; Full time

Job summary

Acaisoft poszukuje Lead Data Engineera do pracy nad potężnymi pipeline'ami danych i zastosowaniami AI/ML w projekcie dla amerykańskiego klienta BioPharma. Rola obejmuje projektowanie i utrzymanie skalowalnych rozwiązań data platform, od gromadzenia danych po udostępnianie wyników analitycznych, z naciskiem na jakość i zaufanie danych.

Wymagane umiejętności to Python, SQL, Spark na Databricks, modelowanie danych oraz integracja źródeł relacyjnych i dokumentów; praca w międzynarodowym środowisku,

Qualifications

• Minimum seniority as Lead/Principal Data Architect in multinational environments.
• Deep knowledge of data modelling, integration, warehousing and modern data platforms.
• Strong Python and SQL with production Spark on Databricks for analytics/AI use cases.
• Experience with structured and unstructured data across ingestion, transformation and storage.

Responsibilities

• Build and operate data ingestion/processing for structured, semi-structured, and unstructured data; support prototyping to production.
• Implement metadata and data quality practices enabling cross-record querying and AI-ready data access.
• Collaborate with architects, AI/engineering teams and domain experts to ensure usable, performant data for GenAI and analytics.
• Mentor junior/mid-level engineers and support code reviews.
• Lead architectural decisions and engage with key stakeholders.

Skills

Lead Data Architect

Data modelling

Data integration

Data warehousing

Databricks / Spark

Python

SQL

CI/CD

Metadata & data quality

Azure / cloud data platforms

Tools

Databricks

Unity Catalog

Delta Lake

Oracle

PostgreSQL

MongoDB

Job description

O projekcie: Hi there! If you’re looking for a high-impact position in an ambitious software house, we’ve got a match for you!

Currently, we are searching for a full-time Lead Data Engineer to cooperate with our US client - BioPharma organization. The mission is to unlock the potential of AI/ML to improve the lives of patients. Our client delivers powerful cloud software and services specifically designed to meet the evolving needs of the BioPharma sector.

You will be an integral part of the hands‑on delivery of reliable, scalable data pipelines and datasets that power AI discoverability, analytics, reporting, and workflow automation across scientific, clinical, and enterprise data.

Due to the client's location, the team is working till 6:00 PM CEST.

Wymagania:

• Experience as a Lead / Principal Data Architect in multinational environments.
• Deep knowledge of data modelling, integration, warehousing, and modern data platforms.
• Strong hands‑on Python and complex SQL, with production Spark experience on Databricks (Structured Streaming, Auto Loader, Delta Lake, Unity Catalog) to support analytics, AI/ML, and enterprise application use cases.
• Practice working with structured and unstructured data across the full lifecycle: ingestion, transformation, enrichment, indexing, and retention, delivered through disciplined engineering practice including infrastructure‑as‑code, CI/CD, and automated testing of pipelines.
• Proven experience ingesting from heterogeneous operational sources including relational (Oracle, PostgreSQL) and document stores (MongoDB), using Full load + CDC or equivalent replication patterns, with practical handling of schema drift, deletes, and late or out‑of‑order data.
• Skills in data modelling across a layered architecture, with clear contracts between raw, curated and serving layers, supporting cross‑entity queries and contextual linking, and performant data access patterns.

Nice to have:

• Experience building unstructured document pipelines: parsing and extraction from PDF, Office and scanned formats, chunking, embedding generation, and maintaining searchable indexes at scale.

Codzienne zadania:

• Build, evolve, and operate data ingestion and processing capabilities for structured, semi-structured, and unstructured data, supporting the transition from early prototypes through early adoption and general release.
• Implement and maintain rich metadata and data quality practices that enable cross‑record querying, traceability, and AI‑ready data access across experiments, files, inventory, and workflows.
• Work with architects, AI engineers, workflow engineers, and domain experts to ensure data is usable, performant, and trustworthy for downstream GenAI and analytics use cases.
• Support engineering quality through code reviews and mentoring of junior/mid-level engineers.
• Lead architectural decisions and work with senior business and technology stakeholders.Guide and mentor data and engineering teams