Databricks Data Engineer

Accenture in the Philippines; Cebu City; On-site; PHP 600,000 - 1,200,000; Full time

Job summary

Accenture in the Philippines is seeking a data engineer to design, develop and maintain data solutions for data generation, collection, and processing using Databricks.

You will build high-quality ETL/ELT pipelines with Delta Lake and Auto Loader, using PySpark and Python, ensuring data quality, security with Unity Catalog, and seamless BI tool integration (Power BI/Tableau/Looker).

Qualifications

• 5+ years of data engineering across cloud platforms and data warehousing.
• Experience building ETL/ELT pipelines on Databricks (Delta Lake, Auto Loader, DLT).
• Proficient in Python and PySpark for transformations and validation.
• Knowledge of Unity Catalog and data security controls.
• Familiarity with BI/visualization tool integration (Power BI/Tableau/Looker).

Responsibilities

• Develop scalable ETL/ELT pipelines on Databricks with Delta Lake and DLT.
• Implement ingestion patterns using Auto Loader with schema evolution.
• Create modular dbx functions for transformations and PII masking.
• Build secure, observable DLT pipelines with medallion layering and auditing.
• Integrate Databricks with Power BI/Tableau/Looker using connectors.

Skills

Python

PySpark

Data pipelines

Performance optimization

Cloud platforms

Tools

Databricks

Delta Lake

Auto Loader

DLT

Unity Catalog

Power BI

Tableau

Looker

ODBC/JDBC

Delta Sharing

dbt

Informatica

Talend

Matillion

Fivetran

Spark

Hadoop

Kafka

Airflow

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.

Key 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.
• At least 2 years of relevant work 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: