Databricks / Delta Lake Tech Lead + Business Analyst

Roles & Responsibilities

Defines, prioritizes and allocates resources; tracks and provides status reports of work assignments, projects, and programs performed in support of the Data Engineering team.
Lead design, development, and implementation of end-to-end complex automated data pipeline system using common Big Data Tools.
Design and implement robust data architectures and data pipelines for large-scale data processing.
Review business and technical requirements and ensure the data integration platform meets requirements
Identifying bottlenecks and optimizing data processing and storage systems for improved performance and efficiency implement monitoring and measurement of data quality as defined by business rules
Collaborating with other departments such as data science, analytics, and IT to understand their data needs and requirements, and aligning data engineering efforts to support organizational goals and initiatives.
Ensuring compliance with data privacy regulations and implementing robust security measures to protect sensitive data from unauthorized access or breaches.

Databricks / Delta Lake Tech Lead + Business Analyst

15 or more years of application delivery experience in the financial services industry
10 or more years of experience in managing complex projects involving the implementation of digital or online-transaction solutions
Experienced in handling vendor and third-party SI engagements
Expertise in database technologies (SQL, NoSQL, etc.) and data warehouse platforms (e.g., Redshift, Snowflake).
Experience with ETL tools and data pipeline frameworks.
Candidate must be willing to work in Ortigas or Makati.

Key Performance Metrics:

1. Requirements assessment — Ability to grasp and translate new requirements quickly and accurately on the first pass, minimizing repeated clarifications, revisions, and requirement-related rework

2. Delivery Commitment — On time delivery of committed milestones and approved requirements

3. Architecture and requirements quality — Full transparency to implementation from business requirements to implementation including solution designs, data models, and acceptance criteria. Designs should pass architecture, data governance, technical review

4. Engineering outcome quality — no critical design or security defects at production release, and agreed performance, reliability, and data-quality thresholds achieved.

5. Incident resolution: Incidents resolved within SLA and recurring incidents supported by root-cause remediation