Databricks Unified Data Analytics Platform Engineer- Cebu

Accenture in the Philippines; Cebu City; On-site; PHP 800,000 - 1,100,000; Full time

Job summary

Accenture in the Philippines is seeking a data engineering professional to design, develop and maintain data solutions for data generation, collection and processing. You will create data pipelines, ensure data quality, and implement ETL processes to migrate and deploy data across systems.

You will develop scalable ETL/ELT pipelines with Databricks technologies (Delta Lake, Auto Loader, DLT), write Python and PySpark code, and work across AWS, GCP, or Azure cloud environments.

Qualifications

• Proficient in Python and PySpark for data transformation and validation.
• Experienced with Databricks Delta Lake, Auto Loader, and Delta Live Tables (DLT).
• Hands-on with at least one cloud platform: AWS, GCP, or Azure.
• Ability to build modular transformation functions and reusable pipelines.
• Strong understanding of data warehousing concepts and data modeling.
• Experience with data orchestration and CI/CD practices in a cloud environment.

Responsibilities

• Develop scalable ETL/ELT pipelines using Databricks and Delta features.
• Write high-quality Python code with debugging and optimization.
• Implement ingestion patterns with Auto Loader and checkpointing.
• Build secure, observable DLT pipelines with Bronze/Silver/Gold layers.
• Configure Unity Catalog and manage data access controls and masking.
• Create SQL dashboards and BI connectors to Databricks (ODBC/JDBC).
• Prepare datasets for GenAI workflows and feature stores.
• Collaborate with stakeholders on Delta Sharing and data sharing.

Skills

Python programming

PySpark

Databricks

Delta Lake

Auto Loader

DLT (Delta Live Tables)

Cloud platforms

CI/CD

SQL

Performance optimization

Tools

Databricks

Unity Catalog

Power BI/Tableau/Looker

ODBC/JDBC connectors

GitHub/GitLab CI/CD

Job description

Job Description:

Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.

Responsibilities:

• Develop high-quality, scalable ETL/ELT pipelines using Databricks technologies including Delta Lake, Auto Loader, and DLT.
• Excellent programming and debugging skills in Python.
• Strong hands‑on experience with PySpark to build efficient data transformation and validation logic.
• Must be proficient in at least one cloud platform: AWS, GCP, or Azure.
• Create modular dbx functions for transformation, PII masking, and validation logic — reusable across DLT and notebook pipelines.
• Implement ingestion patterns using Auto Loader with checkpointing and schema evolution for structured and semi‑structured data.
• Build secure and observable DLT pipelines with DLT Expectations, supporting Bronze/Silver/Gold medallion layering.
• Configure Unity Catalog: set up catalogs, schemas, user/group access, enable audit logging, and define masking for PII fields.
• Enable secure data access across domains and workspaces via Unity Catalog External Locations, Volumes, and lineage tracking.
• Access and utilize data assets from the Databricks Marketplace to support enrichment, model training, or benchmarking.
• Collaborate with data sharing stakeholders to implement Delta Sharing — both internally and externally.
• Integrate Power BI/Tableau/Looker with Databricks using optimized connectors (ODBC/JDBC) and Unity Catalog security controls.
• Build stakeholder‑facing SQL Dashboards within Databricks to monitor KPIs, data pipeline health, and operational SLAs.
• Prepare GenAI‑compatible datasets: manage vector embeddings, index with Databricks Vector Search, and use Feature Store with MLflow.
• Package and deploy pipelines using Databricks Asset Bundles through CI/CD pipelines in GitHub or GitLab.
• Troubleshoot, tune, and optimize jobs using Photon engine and serverless compute, ensuring cost efficiency and SLA reliability.
• Experience with cloud‑based services relevant to data engineering, data storage, data processing, data warehousing, real‑time streaming, and serverless computing.
• Hands on Experience in applying Performance optimization techniques
• Understanding data modeling and data warehousing principles is essential.

Good to Have:

• Certifications: Databricks Certified Professional or similar certifications.
• Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries.
• Knowledge of big data processing (e.g., Spark, Hadoop, Hive,Kafka)
• Data Orchestration: Apache Airflow.
• Knowledge of CI/CD pipelines and DevOps practices in a cloud environment.
• Experience with ETL tools like Informatica, Talend, Matillion, or Fivetran.
• Familiarity with dbt (Data Build Tool)

Requirements: