Data Technical Lead

NIX Tech

Standszám:

C29

NIX, a global supplier of software engineering and IT outsourcing services, is looking for a Data Technical Lead in its office in Budapest (Vaci Greens, 13th district). You’ll be part of a team of professionals who are ready to find the best tailor-made IT solutions for their multinational clients in various industries and solve complex problems.

RESPONSIBILITIES:

Lead the design and development of data engineering solutions, including data pipelines and ETL processes, leveraging your technical expertise.
Lead and mentor a team of data engineers, providing technical guidance and oversight to ensure high-quality deliverables.
Accurately estimate time and resources required for new projects.
Promote a continuous learning and improvement culture, staying up-to-date with industry best practices and emerging technologies.
Evaluating team performance and providing feedback and coaching.
Encouraging collaboration and open communication within the team and with other stakeholders.
Participating in the recruitment process to select new team members.

WHAT WE EXPECT FROM YOU:

Strong technical skills in code review, architecture, and data models.
Experience with designing data pipelines and building ETL processes.
Passion for continuous learning and improvement.
Ability to accurately estimate time and resources for projects.
Ability to manage timelines and deadlines while ensuring high-quality work.
Excellent communication and collaboration skills.
Ability to identify and address performance issues and recognize high performers.
Strong leadership and mentoring skills.
Experience in recruiting and building a team.

REQUIRED SKILLS:

6+ years of proven experience developing software using an object-oriented or a functional language.
Minimum English level B2.
Strong programming skills in Python or Scala/Java.
Solid with distributed computing approaches, patterns, and technologies (Spark/PySpark).
Experience working with Cloud Platforms (GCP, AWS, Azure – any of the listed) and their data-oriented components.
Proficiency in SQL and query tuning.
Deep knowledge of data warehousing principles and modeling concepts (knowledge of data model types and terminology including OLTP/OLAP, SCD,
(de)normalization, dimensional, star).
Experience in modern data warehouse building using modern tools (e.g., Snowflake, AWS Redshift, BigQuery).
Expertise in the use of any relational databases (e.g., PostgreSQL, MSSQL, MySQL).
Experience with any tool for data flow orchestration (e.g., Apache Airflow, Prefect, Glue, Azure Data Factory).
A team player with excellent collaboration skills.

WILL BE A PLUS:

Expertise in data storage design principles. Understanding of pros and cons of SQL/NoSQL solutions, their types, and configurations (standalone/cluster, column/row-oriented, key-value/document stores).
Deep knowledge of Spark internals (tuning, query optimization).
Experience with data integration and business intelligence architecture.
Experience with non-relational databases (e.g., MongoDB, DynamoDB).
Experience with data lakes and lake-houses (Azure Data Lake, Apache Hudi, Apache Iceberg, Delta Lake).
Experience with stream processing using the current industry standards (e.g., AWS Kinesis, Kafka streams, etc.).
Experience with distributed computing in the Hadoop ecosystem (Beam, Flink, Storm, HDFS, and Hive).
Experience with containerized (Docker, ECS, Kubernetes) or serverless (Lambda) deployment.
Good knowledge of popular data standards and formats (e.g., JSON, XML, Proto, Parquet, Avro, ORC, etc.).
Experience with Platforms: Informatica, Databricks, Talend, Fivetran, or similar.
Experience in data science and machine learning with building Machine Learning models.