Databricks Unified Data Analytics Platform Engineer

Accenture; Philippines; On-site; PHP 600,000 - 1,800,000; Full time

Job summary

Accenture in Philippines is seeking a data engineer to design, develop, and maintain scalable data pipelines using Databricks, Delta Lake, Auto Loader, and DLT. You will implement ETL processes, ensure data quality, and enable secure data access across environments.

You'll work with PySpark, Python, SQL dashboards, and BI connectors (Power BI/Tableau/Looker) to monitor KPIs and SLA health. Experience with Unity Catalog, Delta Sharing, and CI/CD workflows in GitHub or GitLab is highly valued.

Qualifications

• Strong Python and PySpark programming skills.
• Hands-on Databricks experience with Delta Lake, Auto Loader, and DLT.
• Proficient in at least one cloud platform: AWS, GCP, or Azure.
• Experience building scalable ETL/ELT pipelines and data quality checks.

Responsibilities

• Design, develop and maintain data pipelines for generation, collection and processing.
• Implement ETL/ELT processes across systems and ensure data quality.
• Build secure, observable DLT pipelines with Bronze/Silver/Gold medallion zoning.
• Configure Unity Catalog for catalogs, schemas, access control and masking.
• Enable cross-domain data access via Unity Catalog External Locations and lineage.
• Create SQL dashboards in Databricks to monitor KPIs and SLAs.
• Integrate BI tools (Power BI/Tableau/Looker) via optimized connectors.
• Package and deploy pipelines using Databricks Asset Bundles in CI/CD.
• Troubleshoot and optimize jobs using Photon engine and serverless compute.

Skills

Python

PySpark

Databricks

SQL

ETL/ELT

Data modeling

Performance tuning

Cloud platforms

CI/CD

Education

Databricks Certified Professional

Tools

Delta Lake

Auto Loader

DLT

Unity Catalog

Delta Sharing

ODBC/JDBC connectors

GitHub/GitLab CI/CD

MLflow

Databricks Asset Bundles

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

1. Certifications: Databricks Certified Professional or similar certifications.
2. Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries.
3. Knowledge of big data processing (e.g., Spark, Hadoop, Hive,Kafka)
4. Data Orchestration: Apache Airflow.
5. Knowledge of CI/CD pipelines and DevOps practices in a cloud environment.
6. Experience with ETL tools like Informatica, Talend, Matillion, or Fivetran.
7. Familiarity with dbt (Data Build Tool)
   Minimum 3 year(s) of experience is required

#LI-PH

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com

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