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Data Engineering Consultant

Company Description

Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
 
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
 
From prototype to real-world impact - be part of a global shift by doing work that matters.

Job Description

Role Overview

A Data Engineering Consultant designs, implements, and optimizes scalable data pipelines and architectures. This role bridges raw data and actionable insights, ensuring robustness, performance, and data governance. Collaboration with analysts and scientists is central to delivering high-quality solutions aligned with business objectives.

Key Responsibilities

  • Data Pipeline Development
  • Architect, implement and maintain real-time and batch data pipelines to handle large datasets efficiently.
  • Employ frameworks such as Apache Spark, Databricks, Snowflake or Airflow to automate ingestion, transformation, and delivery.
  • Data Integration & Transformation
  • Work with Data Analysts to understand source-to-target mappings and quality requirements.
  • Build ETL/ELT workflows, validation checks, and cleaning steps for data reliability.
  • Automation & Process Optimization
  • Automate data reconciliation, metadata management, and error-handling procedures.
  • Continuously refine pipeline performance, scalability, and cost-efficiency.
  • Collaboration & Leadership
  • Coordinate with Data Scientists, Data Architects, and Analysts to ensure alignment with business goals.
  • Mentor junior engineers and enforce best practices (version control, CI/CD for data pipelines).
  • Participate in technical presales activities and client engagement initiatives.
  • Governance & Compliance
  • Apply robust security measures (RBAC, encryption) and ensure regulatory compliance (GDPR).
  • Document data lineage and recommend improvements for data ownership and stewardship.

Qualifications

  • Programming: Python, SQL, Scala, Java.
  • Big Data: Apache Spark, Hadoop, Databricks, Snowflake, etc.
  • Cloud: AWS (Glue, Redshift), Azure (Synapse, Data Factory, Fabric), GCP (BigQuery, Dataflow).
  • Data Modelling & Storage: Relational (PostgreSQL, SQL Server), NoSQL (MongoDB, Cassandra), Dimensional modelling.
  • DevOps & Automation: Docker, Kubernetes, Terraform, CI/CD pipelines for data flows.
  • Architectural Competencies
  • Data Modelling: Designing dimensional, relational, and hierarchical data models.
  • Scalability & Performance: Building fault-tolerant, highly available data architectures.
  • Security & Compliance: Enforcing role-based access control (RBAC), encryption, and auditing.

Additional Information

Discover some of the global benefits that empower our people to become the best version of themselves:

  • Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus;  
  • Career Development: Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership;
  • Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences;
  • Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme;
  • Health: Global internal wellbeing programme, access to wellbeing apps;
  • Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.

Our diversity makes us stronger - it drives meaningful change and enables us to build innovative technology solutions. We are committed to creating an inclusive community where all of us, regardless of background, identity, or personal characteristics, feels valued, respected, and free from discrimination. As an equal opportunity employer, we welcome applications from all individuals and base hiring decisions on merit, skills, qualifications, and potential.

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What You Should Know About Data Engineering Consultant, Endava

Are you ready to take your career to the next level as a Data Engineering Consultant in Zagreb, Croatia? At our company, we believe that technology is our how and people are our why. With over two decades of experience, we’re on a mission to drive meaningful change through innovative and intelligent digital experiences. As a Data Engineering Consultant, you will be a key player in designing, implementing, and optimizing data architectures and pipelines that turn raw data into actionable insights. You will collaborate with our brilliant Data Scientists and Analysts to ensure that your solutions not only meet business objectives but are also scalable and efficient. Your expertise will shine as you work with industry-leading frameworks like Apache Spark, Databricks, and Snowflake to automate data flow and enhance data quality. This role is also about leadership; you will mentor junior engineers, participate in client engagement initiatives, and enforce data governance principles. With our commitment to career development, hybrid work options, and a supportive community, we offer an environment where you can thrive and make an impact. Join us and be part of a global shift - your next big opportunity awaits!

Frequently Asked Questions (FAQs) for Data Engineering Consultant Role at Endava
What are the main responsibilities of a Data Engineering Consultant at our company?

As a Data Engineering Consultant, your primary responsibilities will include designing, implementing, and maintaining scalable data pipelines and architectures. You'll work closely with Data Analysts to develop ETL/ELT workflows, ensure data quality, and automate processes for efficiency. Collaborating with Data Scientists and mentoring junior engineers will also be crucial to aligning solutions with business objectives.

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What qualifications are necessary for the Data Engineering Consultant position?

To excel as a Data Engineering Consultant, candidates should have strong programming skills in Python, SQL, Scala, or Java, along with experience in Big Data tools such as Apache Spark, Databricks, or Snowflake. Knowledge of cloud platforms like AWS, Azure, or GCP, and expertise in data modeling and DevOps automation are also important for this role.

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How does the company support career development for Data Engineering Consultants?

Our company offers robust career development programs for Data Engineering Consultants, including career coaching, access to complex projects, and opportunities for non-linear career paths. We're dedicated to fostering your growth through internal development programs aimed at enhancing both management and technical skills.

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What does a typical work environment look like for a Data Engineering Consultant here?

As a Data Engineering Consultant, you'll enjoy a hybrid work environment with flexible hours, enabling you to balance your professional and personal life effectively. Our company emphasizes collaboration, inclusion, and a supportive community through internal tech communities and work-life balance initiatives, creating a positive atmosphere to thrive in.

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What values does the company prioritize for its Data Engineering Consultants?

Our company values diversity, innovation, and a people-centric mindset. We prioritize creating an inclusive community where every Data Engineering Consultant feels valued and respected, fostering an environment that allows for collaboration and meaningful technological change.

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Common Interview Questions for Data Engineering Consultant
Can you explain your experience with data pipeline development?

When answering this question, highlight specific projects where you've developed data pipelines. Discuss the technologies you used, such as Apache Spark or Airflow, and the outcomes of those projects, emphasizing your role in optimizing performance.

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What frameworks have you used for data integration?

Be prepared to discuss lifecycle examples using frameworks like Snowflake or Databricks. Explain how these tools helped you compile and transform data efficiently, making sure to include details about the data quality measures you implemented.

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How do you ensure compliance with data governance standards?

Detail your experience applying security measures like RBAC and encryption, and how you adhere to regulations such as GDPR. Provide examples of how you've documented data lineage and implemented improvements for data stewardship.

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Describe a time when you collaborated with Data Scientists or Analysts.

Share a scenario where your collaboration significantly impacted a project’s outcome. Discuss how you communicated technical complexities and aligned your data processes with their analytical requirements, emphasizing teamwork and mutual goal achievement.

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How do you keep yourself updated with emerging data technologies?

Discuss your strategies for continuous learning, such as attending conferences, participating in webinars, or taking online courses. Highlight any specific tools or trends you've recently explored and how they've influenced your work.

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What challenges have you faced in data automation, and how did you overcome them?

Provide an example of a challenge you've encountered in data reconciliation or pipeline automation. Describe the steps you took to identify the problem, implement a solution, and the result of that solution, highlighting your problem-solving skills.

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What role do you think data engineers play in business decision-making?

Explain how data engineers provide the foundation for data-driven decisions by ensuring reliability and accessibility of data. Discuss the impact high-quality data has on strategic initiatives and how you view your role within that process.

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Can you describe your experience with cloud data solutions?

Share your practical experience with cloud platforms like AWS, Azure, or GCP. Discuss specific use cases where you deployed data solutions, emphasizing the advantages you've observed in performance and scalability.

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How do you manage and prioritize multiple data projects?

Discuss your approach to project management, including tools or methodologies you use to organize tasks. Highlight how you ensure deadlines are met while maintaining quality and how you adapt to shifting priorities effectively.

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What is your approach to mentoring junior engineers in data engineering?

Explain your perspective on mentorship, including how you offer guidance on best practices and technical capabilities. Share specific experiences where your mentorship positively impacted a junior engineer's growth or project success.

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We have built our organisation around a simple philosophy: We focus on helping people to be successful. The people who work for us, the people who engage with us, and the people who use the systems and applications we design, build and operate.

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DATE POSTED
April 23, 2025

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