Databricks Data Engineer

Accenture; Poland; On-site; PLN 180,000 - 300,000; Full time

Job summary

Accenture Poland is seeking a Databricks-focused data engineer to join our Data Engineering practice. You will design and build modern data products and GenAI solutions on Databricks, collaborating with AI experts to deliver production-grade data pipelines.

You will work with PySpark, Spark SQL, Delta Lake, and Lakehouse concepts, implementing CI/CD through Git in cloud environments (AWS/GCP/Azure). The role emphasizes ETL/ELT development, data modeling, and collaboration with architects and

Qualifications

• 2+ years hands-on experience with SQL, Python or Spark in production environments.
• 1 year practical experience designing or implementing ETL/ELT using Databricks and Apache Spark in cloud environments.
• Exposure to data modelling and architecture: conceptual, logical and physical data models.

Responsibilities

• Design and implement scalable end-to-end data pipelines (ETL/ELT) using Databricks and Spark in cloud environments.
• Develop and maintain data models (Dimensional, relational, Data Vault) for analytics-ready datasets.
• Build data transformations using PySpark, Spark SQL and Delta Lake.
• Integrate diverse data sources into unified analytics datasets and collaborate with data scientists.
• Implement DevOps/DataOps practices including Git, CI/CD, and testing of data workflows.

Skills

SQL

Python

Spark

ETL/ELT

Data modeling

CI/CD

Git

Cloud data engineering

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Delta Lake

Lakehouse

Job description

Databricks Community of Practice

Join our Databricks Community of Practice in Poland, where delivery excellence is at the core of everything we do. We design and build modern data products as well as advanced GenAI and agentic solutions powered by Databricks.

As part of a global network of 9,000+ AI experts and data scientists, we collaborate with leading technology partners—including Databricks, AWS, Google Cloud, Microsoft, Snowflake, and SAS to deliver scalable, enterprise-grade data solutions that generate measurable business impact.

• Design and implement scalable end-to-end data pipelines (ETL/ELT) using Databricks (batch and streaming), including modern orchestration patterns such as Delta Live Tables
• Develop and maintain data models using Lakehouse and Medallion architecture (Bronze, Silver, Gold layers).
• Build robust data transformations using PySpark, Spark SQL, and Delta Lake.
• Integrate diverse cloud and enterprise data sources into unified, high-quality, analytics-ready datasets.
• Collaborate with architects, analysts, and data scientists to deliver production-grade data products.
• Implement DevOps and DataOps practices, including Git-based version control, CI/CD pipelines, and testing of data workflows.

HERE’S WHAT YOU’LL NEED:

Experience Requirements (Cumulative):

• At least 2 years of hands‑on experience working with relational or analytical databases, applying SQL, Python or Spark for development, testing, debugging, and performance optimization in production environments.

• Within that experience, at least 1 year practical experience designing or implementing ETL/ELT processes using Databricks and Apache Spark in cloud‑based environments.

• Exposure to data modelling and architecture: practical experience creating conceptual, logical and physical data models using dimensional, relational or Data Vault techniques in analytical environments.

Technical Skills:

Databricks & Spark:

• Hands‑on experience working with Databricks platform, including Delta Lake and Lakehouse architecture concepts.

• Practical knowledge of Apache Spark (PySpark, Spark SQL), including batch and streaming processing.

• Understanding of Medallion architecture design patterns.

Databases & SQL:

• Strong hands‑on SQL skills in analytical and distributed data environments (e.g., Spark SQL).

• Ability to profile, tune, and optimize SQL queries for large-scale data processing workloads.

Data Modeling & ETL

• Solid understanding of dimensional modelling; ability to translate business requirements into conceptual, logical and physical models.

• Hands‑on experience building scalable ELT/ETL pipelines in modern cloud‑native environments.

Cloud & Big Data (Experience or Interest):

• Experience with, or strong interest in, processing and integrating data on major cloud platforms (GCP, Azure, AWS).

• Familiarity with cloud storage, managed databases and serverless / data pipeline services is desirable.

• Understanding of CI/CD pipelines and version control (Git) in data engineering projects

Nice to Have:

• Experience with Dat