Data Engineering Manager

As part of Data & AI practice, you will combine AI & ML with data, analytics and automation under a bold strategic vision to transform business in a very pragmatic way, sparking digital metamorphoses. There will never be a typical day and you will continuously learn and grow. The opportunities to make a difference within exciting client initiatives are unlimited in the ever-changing technology landscape.

You will lead multidisciplinary teams (onshore and offshore) to design and implement solutions using Python, PySpark, Databricks and modern cloud services, ensuring data flows are secure, reliable, performant, and well‑governed.

Responsibilities:

• Lead and mentor a technical team (5–8 engineers) in designing and delivering data‑engineering solutions across cloud platforms (Azure/AWS/GCP).
• Architect, develop, and optimize ETL/ELT pipelines using Python, PySpark, Databricks, and distributed processing frameworks.
• Design and maintain scalable data architectures, including data lakes, lakehouses, warehouses, Delta tables, and semantic layers.
• Establish best practices in data modelling (dimensional, wide‑table, data vault), data quality, metadata management, and data governance.
• Collaborate with business owners, architects, product managers, and analytics teams to ensure end‑to‑end project delivery.
• Define standards for data operations, monitoring, lineage, and CI/CD for data workloads.
• Drive cloud platform adoption and modernization, ensuring solutions meet performance, cost‑efficiency, and compliance requirements.
• Promote innovation by evaluating new data engineering tools, frameworks, and practices.
• Mentor junior team members, providing guidelines to ensure high-quality deliverables.
• Communicate complex technical solutions to senior management and diverse stakeholders effectively.
• Contribute to sales activities through data and platform architecture expertise
• Stay updated on industry trends and contribute to internal initiatives, R&D, and business development projects.

WHO WE’RE LOOKING FOR?

• BSc/MSc/PhD in Computer Science, Engineering, Information Systems, Data Management, or related field.
• 7+ years of experience in data engineering, with at least 2 years leading small technical teams.
• Industry vendor certifications are desired (e.g. AWS, Azure, GCP, CNCF/Kubernetes or Databricks certifications); although not essential if you have demonstrable ability.
• Strong hands‑on expertise with Python, PySpark, and Databricks (including Lakehouse & Databricks Workflows).
• Experience designing and deploying data solutions on Azure, AWS, or GCP.
• Strong understanding of data modelling techniques, data lifecycle management, and enterprise data principles.
• Expertise in designing, developing, and managing scalable, end-to-end data pipelines (ADF, Airflow or dbt,).
• Proficient in Big Data Platforms (Hadoop, Databricks, Hive, Kafka, Apache Iceberg or Microsoft Fabric), Data Warehouses (Teradata, Snowflake, BigQuery etc.) and lakehouses (Delta Lake, Apache Hudi)
• Proficient in programming languages such as SQL, Python and Pyspark with strong skills in writing scalable, readable and maintainable code using object-oriented programming concept.
• Implement DevOps practices, including Git workflows and CI/CD pipelines (Azure DevOps, Jenkins, GitHub Actions) to enhance automation and streamline deployments.
• Experience in project management frameworks such as Waterfall or Agile.
• Solid experience with SQL and handling large datasets.
• Familiarity with data governance frameworks (e.g., data quality controls, cataloging, lineage, roles & policies).
• Excellent communication skills—able to translate complex data concepts to both technical and non‑technical stakeholders.