Location: Offshore

Job Summary:

We are seeking a highly skilled Technical Architect with a strong focus on Databricks to design and implement scalable data platforms and pipelines. The ideal candidate will lead the migration from legacy systems to a modern Lakehouse architecture, ensuring data governance, quality, and security while collaborating with various stakeholders to align technical solutions with business objectives.

Years of Experience : 8 - 10 yrs

Core Responsibilities:

Design and implement scalable data platforms and pipelines using Azure Databricks, Apache Spark, Delta Lake, and MLflow.
Lead the migration from legacy platforms to Lakehouse architecture.
Develop batch and streaming data pipelines for ingestion, transformation, and analytics.
Establish standards for data governance, quality, and security.
Collaborate with stakeholders to align architecture with business goals.
Mentor data engineers and developers on Databricks best practices.
Integrate Databricks with tools like Power BI, Tableau, Kafka, Snowflake, and Azure Data Factory.

Mandatory Skills:

Strong command of Databricks and Azure.
Proficiency in SQL, Python, Scala, and Spark.
Experience with CI/CD pipelines, DevOps, and orchestration tools like Airflow or Data Factory.
Familiarity with Azure cloud platforms.
Deep understanding of distributed computing, performance tuning, and data security.

Preferred Skills:

Experience with data mesh, data fabric, or enterprise data architectures.
Strong ERP knowledge (e.g., SAP, Salesforce).
Hands-on capability and self-exploration skills to drive analytics adoption across business teams.

Qualifications:

Bachelor's or Master's in Computer Science, Data Science, or a related field.
Certifications such as Azure Solution Architect, Azure Data Engineer, or Databricks Certified Data Engineer or Solutions Architect.
Domain knowledge in finance, healthcare, or public sector is a plus.

Join our team and play a pivotal role in transforming our data architecture while mentoring the next generation of data engineers. Your expertise in Databricks will be crucial in driving our data initiatives forward.

About Mphasis:

Mphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis Front2Back™ Transformation approach. Front2Back™ uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis Service Transformation approach helps shrink the core through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients.

Equal Opportunity Employer:

Mphasis is an equal opportunity/affirmative action employer. We provide equal employment opportunities to applicants and existing associates and evaluate qualified candidates without regard to race, gender, national origin, ancestry, age, color, religious creed, marital status, genetic information, sexual orientation, gender identity, gender expression, sex (including pregnancy, breast feeding and related medical conditions), mental or physical disability, medical conditions military and veteran status or any other status or condition protected by applicable federal, state, or local laws, governmental regulations and executive orders. View the EEO in the law poster here, view the EEO in the law supplement here. To view the pay transparency nondiscrimination provision please click here and to view the E-Verify posting click here.
Mphasis is committed to providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of disability to search and apply for a career opportunity, please send an email to [HIDDEN TEXT] and let us know your contact information and the nature of your request.

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