Database Architect

Softeq; Warszawa; On-site; PLN 120,000 - 240,000; Full time

Job summary

Softeq is seeking a Data Engineer to manage large data sets, prep data for ML pipelines, and optimize storage. You will build scalable data pipelines, write advanced SQL, and create dashboards for stakeholders.

Collaboration across engineering, product, and business units will be key to delivering robust data solutions. The ideal candidate has strong SQL, Python or R skills, experience with Airflow/dbt/NiFi, and familiarity with cloud data platforms and BI tools.

Qualifications

• Proven SQL proficiency and ability to write optimized queries.
• Experience with large-scale data environments and data warehousing.
• Programming in Python or R for data processing.
• Familiarity with ETL tools and BI platforms.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL processes.
• Write complex SQL queries for analytics from multiple sources.
• Build dashboards and reports for business insights.
• Collaborate with engineering, product, and business units.
• Perform exploratory data analysis to uncover trends.
• Ensure data quality and governance practices.
• Develop automation scripts to streamline workflows.
• Support data infrastructure improvements and document data models.

Skills

SQL proficiency

Data analysis

Python/R

ETL tooling

BI platforms

Data warehousing

Cloud platforms

Communication

Education

Bachelor's degree

Tools

Airflow

dbt

Apache NiFi

Tableau

Power BI

Looker

Job description

Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together!

We are looking for a strong Data Engineer who can work with a large amount of data and prepare it for the ML pipelines, provide data analysis and visualization, and continuously perform storage optimization tasks. Experience with different types of SQL databases(MySQL/PostgreSQL/MSSQL), knowledge of JOINS, inner selects, and index specification/creation to speed up requests.

Responsibilities:

• Design, develop, and maintain scalable data pipelines and ETL processes to support analytics and business intelligence;
• Write complex SQL queries to extract, transform, and analyze large datasets from multiple sources;
• Build and maintain dashboards, reports, and other data visualizations to deliver actionable insights to business stakeholders;
• Collaborate with cross-functional teams, including engineering, product, and business units, to understand data needs and deliver robust solutions;
• Perform exploratory data analysis (EDA) to uncover trends, anomalies, and opportunities for deeper investigation;
• Ensure data quality and integrity through rigorous validation and data governance practices;
• Develop scripts and tools using languages like Python or R to automate workflows and conduct advanced data analyses;
• Support data infrastructure improvements and contribute to the overall data architecture and design;
• Document processes, data models, and pipelines for internal knowledge sharing and future reference.

Requirements:

• Proficiency in SQL with the ability to write efficient, optimized queries for complex datasets;
• Strong analytical skills with experience working with large-scale data environments (e.g., data warehouses, relational databases);
• Programming experience in Python, R, or another data-focused language;
• Familiarity with ETL tools and frameworks (e.g., Airflow, dbt, Apache NiFi);
• Experience with BI platforms such as Tableau, Power BI, Looker, or similar;
• Solid understanding of data modeling, data warehousing concepts, and database design;
• Experience working with cloud-based data platforms (e.g., AWS Redshift, Google BigQuery, Snowflake);
• Strong problem-solving abilities and attention to detail;
• Excellent communication skills with the ability to translate technical findings into business insights;
• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related field (or equivalent work experience).