Key Responsibilities & Accountabilities:

As a Data Engineering Head, you will be responsible for leading a team of data engineers in building, optimizing, and maintaining our data infrastructure and pipelines. You will work closely with stakeholders across data science, BI, and business teams to ensure the delivery of reliable, scalable, and secure data solutions.

Team Leadership and Management: Lead and manage the Data Engineering teams, providing guidance, support, and mentorship to ensure the successful development and maintenance of business intelligence solutions and data architectures.
Architecture Oversight: Design and oversee scalable data architecture, including data lakes, data warehouses, and streaming pipelines.
Data Integration: Ensure seamless integration of data from multiple source systems across structured and unstructured formats.
Data Modeling Standards: Enforce best practices in data modeling (e.g., star schema, Data Vault, SCD), ETL design, and version control.
Platform Operations Monitoring & Optimization: Monitor, troubleshoot, and optimize the performance and reliability of data systems and platforms.
Governance and Quality: Ensure data quality, consistency, lineage, and adherence to governance policies and compliance standards.
Innovation & Automation: Introduce automation, CI/CD, and new technologies to improve the data platform's agility and performance.
Cross-Functional Collaboration: Collaborate with cross-functional teams to integrate and centralize data from various sources into business intelligence platforms, addressing data-related technical issues, and meeting data infrastructure needs.
Collaboration: Act as a key partner with business teams, Data Science, BI, and governance teams to deliver end-to-end data solutions.
Hands on collaboration: Collaborate with the team and work with hands on with team members to ensure projects and exercise are executed smoothly and delivered on the agreed-on SLA, timelines knowledge and ensure knowledge transfer between team members to ensure smooth business flow.
Documentation & Standards: Maintain clear documentation for architecture, pipelines, and processes while enforcing engineering standards.
Talent Development: Support the professional development of team members, identifying training needs, and fostering a collaborative and dynamic environment to leverage data management and analytics expertise.
Project Management: Plan, prioritize, and manage data engineering projects and resource allocation to meet business needs.

Job Specifications:

Business Knowledge/Technical Skills:

Bachelor's or master's degree in computer science, Information Systems, Data Engineering, or a related field.
Minimum of 6+ years of experience in data engineering, with at least 2 years in a leadership or managerial role.
Strong expertise in SQL, data modeling, and ETL pipeline design using tools like Informatica PowerCenter, Alteryx, or SSIS.
Experience with data platforms such as MSSQL, Oracle, PostgreSQL, and big data environments (Hadoop, Spark).
Proficiency in scripting and programming languages such as Python, Java, or Scala
Familiarity with workflow orchestration tools (e.g., Apache NiFi, Airflow, Dagster).
Understanding of CI/CD, DevOps, and DataOps practices in a modern data stack.
Excellent leadership, communication, and stakeholder management skills.
Knowledge of data governance, security, privacy, and compliance frameworks.
In-depth knowledge of data management best practices.
Strong analytical and problem-solving and analytical abilities.
Effective communication and collaboration skills.
Ability to work collaboratively in a dynamic, cross-functional environment.
Experience with cloud data services (Azure Data Factory, AWS Glue, Snowflake, etc.) is a plus.
Insurance or financial services industry experience is preferred.