The Analytics Engineering Technical Lead will define and own the modeling architecture, standards, and technical direction within RSI’s cloud-native Data Platform. This role is accountable for ensuring that analytics-ready data is reliable, scalable, and aligned to long-term business needs across growth, operations, and risk.

You will lead the design and governance of core data models in Snowflake and dbt, establishing best practices that enable high-quality, self-service analytics without sacrificing trust or performance. As a hands-on technical leader, you will set standards for modeling, testing, documentation, and deployment while mentoring other Analytics Engineers and raising the overall technical bar of the team.

This role partners closely with Data Engineering, Analytics, and business stakeholders to translate evolving requirements into durable data products. You will help balance speed and governance, prioritize platform investments, and ensure our data foundation scales with the business.

Our modern data stack includes Snowflake, AWS, Python, Airflow, and dbt.

What You'll Do:

• Lead the design, development, and lifecycle management of curated datasets, dbt models, and analytics data marts aligned to business domains and metrics.
• Establish and evolve standards and best practices for dbt modeling, testing, documentation, and code organization to support long-term scalability.
• Ensure analytical datasets are accurate, reliable, and trusted, serving as a single source of truth for business, product, and stakeholders.
• Embed data quality checks, documentation, and governance directly into analytics pipelines.
• Partner closely with Product, Data Engineering and Analytics teams to translate business requirements into well-modeled, performant data products.
• Mentor and support Analytics Engineers through technical guidance, design reviews, and knowledge sharing.
• Continuously improve and modernize analytics workflows by introducing automation, tooling enhancements, and AI-assisted development where appropriate.
• This role includes technical leadership and mentoring responsibilities for the team, including other Analytics Engineers.

What You'll Bring:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative discipline.
• Technical Leadership: Proven experience defining technical direction, standards, and architectural decisions within analytics engineering or curation teams.
• Data Modeling Expertise: Deep understanding of dbt, data marts, dimensional modeling, and self-service BI enablement.
• Data Engineering Background: 7+ years of experience in data warehousing or development; 5+ years of experience in hands-on data engineering using SQL and Python.
• Cloud Infrastructure: Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization (Docker/Kubernetes).
• Tooling & Orchestration: Experience with tools like dbt, Airflow, S3, REST APIs, and large-scale DWHs (Snowflake, Redshift, BigQuery, etc.).
• Change Management: Skilled at leading cross-functional data initiatives and driving adoption of new tools and standards.
• People Leadership: Experience mentoring and guiding distributed teams across time zones.
• Communication: Strong written and verbal English skills with the ability to collaborate across technical and business functions.
• Able to travel occasionally both domestically and internationally #LI-HYBRID

❗️IMPORTANT: This position is office based for our office in Birkirkara, Malta.

❗️We can provide relocation assistance if you're outside of the city or country.

The Senior Data Operations Engineer will support the management and optimisation of the company’s data systems and processes, reporting directly to the Data Operations Lead.

This role requires a technically proficient individual with strong analytical skills to ensure data integrity, accessibility, and security across the data platform. The ideal candidate will have hands-on experience in data management, a keen eye for detail, and the ability to work collaboratively with different stakeholders.

✅ Responsibilities:

Data Management and Maintenance

• Assist in developing and enforcing policies for effective data management.
• Ensure data accuracy, consistency, and security through regular maintenance and monitoring.
• Support the implementation of data quality standards and maintain data governance practices.

Operations Support

• Manage and optimise data pipelines and ETL processes to ensure smooth and efficient workflows.
• Troubleshoot and resolve data-related issues promptly to minimise downtime.
• Monitor the availability and performance of data systems and tools, ensuring they meet operational standards.

Collaboration and Communication

• Work closely with the Data Operations Lead, Data Engineering, Business Intelligence, Analytics, and Business teams to support data initiatives.
• Collaborate with vendors and partners for data-related services as directed by the Data Operations Lead.
• Communicate effectively with team members and stakeholders to ensure alignment on data projects and initiatives.

Compliance and Security

• Assist in ensuring compliance with various jurisdictions and regulations, as well as industry best practices.
• Implement and maintain data security measures to protect sensitive information.
• Participate in regular audits and assessments to identify and mitigate risks.

Day-to-Day Activities

• Check the status of data jobs to ensure they are running as expected and troubleshoot any issues.
• Monitor and maintain data pipelines, ensuring data flows smoothly between systems.
• Conduct data verification and integrity checks to maintain high data quality and accuracy.
• Perform reconciliations across various data sources to identify and resolve discrepancies.
• Proactively perform data audits to ensure data accuracy and compliance.
• Suggest and build improvements in data accuracy and reconciliation frameworks.
• Work collaboratively with the data team to address any operational challenges and support data-driven initiatives.

On-Call Rotations

• On-Call Rotations - During specific weeks, you need to be available 24/7 in case of incidents.

✅ Requirements:

• At least 3 years of Python engineering experience;
• At least 2 years of experience with SQL;
• 1+ years of experience in data management and data operations roles.
• Proficiency in SQL, Python, or other data manipulation languages.
• Experience with cloud data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
• Familiarity with ETL tools (e.g., Apache NiFi or Airflow).
• Knowledge of cloud platforms (AWS, Azure, Google Cloud).
• Strong problem-solving skills and attention to detail.
• At least 1 year of experience in PowerBI (or similar);
• Excellent communication and teamwork skills.

Nice to have:

• Experience with data quality management.
• Experience working with agile.
• Understanding of data governance frameworks and practices.
• Certifications in relevant data management or cloud platform technologies.

✅ We offer excellent benefits, including but not limited to:

✔️24 days of vacation per year;

✔️Private Health Insurance;

✔️Daily Breakfasts and Friday lunches in the office;

✔️Snacks in the Office;

✔️Transport Allowance of up to 150 euro paid quarterly (public transport or taxi);

✔️Gym Subsidies for office building gym;

✔️Tennis or Paddle Training sessions reimbursement, up to 100 EUR monthly;

✔️Discount for Premium subscription for Cloudigo;

✔️Discounts at partners of the company;

✔️Annual salary reviews;

✔️Birthday gifts;

✔️Option for parking;

✔️Regular office & team building events.