Senior Data Engineer / Senior Data Architect

Lever, Inc.

Hyderabad

Hybrid

INR 4,000,000 - 7,000,000

Full time

3 days ago

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Job summary

Lever, Inc. in Hyderabad is seeking an experienced Senior Data Engineer / Senior Data Architect to design, build, and maintain scalable data solutions using Databricks. You will work with Databricks Delta Tables, PySpark, Python, SQL, and data modeling to deliver reliable datasets for reporting and analytics.

The role blends hands-on engineering with solution design and stakeholder collaboration. Hybrid work: 3 days in the office, with flexible collaboration to meet business needs.

Qualifications

8+ years of IT experience.
Strong hands-on Databricks and PySpark experience.
Experience designing end-to-end data solutions.
Ability to interact with business and technical stakeholders.
Work location Hyderabad; hybrid work model.

Responsibilities

Design, develop, and maintain scalable data pipelines using Databricks, PySpark, Python, and SQL.
Build ETL/ELT pipelines from multiple source systems.
Load and manage data in Databricks Delta Tables.
Design and maintain logical and physical data models (star schema, normalization).
Create end-to-end data architectures and data movement strategies.
Collaborate with business and technical stakeholders to translate requirements.
Implement data governance, quality checks, metadata, and lineage standards.

Skills

Databricks

PySpark

Python

SQL

Delta Lake

ETL/ELT

Data Engineering

Data Modeling

Data Architecture

Data Pipeline Development

Data Transformation

Performance Tuning

Data Quality

Analytical skills

Stakeholder communication

Tools

AWS

Azure

Kafka

Power BI

Tableau

Git

Azure DevOps

Databricks Unity Catalog

Data Governance

Metadata & Lineage

Job description

We are looking for an experienced Senior Data Engineer / Senior Data Architect to design, build, and maintain scalable data solutions using Databricks.

The ideal candidate should have strong hands-on experience in Databricks, PySpark, Python, SQL, Delta Lake, ETL/ELT, and Data Modeling. The candidate will work closely with business and technical stakeholders to understand requirements, design data solutions, build reliable data pipelines, and deliver high-quality datasets for reporting, dashboards, and analytics.

This role requires a combination of hands-on Data Engineering, Data Architecture, Data Modeling, Solution Design, and Stakeholder Management skills.

Key Responsibilities:

Data Engineering & Pipeline Development

Design, develop, and maintain scalable data pipelines using Databricks, PySpark, Python, and SQL.
Build ETL/ELT pipelines to ingest data from internal and external source systems.
Load and manage data in Databricks Delta Tables.
Handle data from multiple and evolving source systems.
Implement data validation, quality checks, error handling, and monitoring.
Troubleshoot and optimize data pipelines for performance, reliability, and scalability.

Data Transformation & Modeling

Analyze source data and business requirements to design appropriate data transformations.
Develop standardized and curated datasets for reporting, dashboards, and analytics.
Design and maintain logical and physical data models.
Apply dimensional modeling concepts such as fact and dimension tables, star schema, and normalization/denormalization.
Ensure data models are scalable and aligned with business requirements.

Solution Architecture

Design end-to-end data architectures and data movement strategies.
Evaluate source systems and integration patterns and recommend appropriate solutions.
Design scalable, secure, maintainable, and reliable data solutions.
Create technical documentation covering architecture, data flows, mappings, and integration processes.
Contribute to reusable data engineering frameworks and best practices.

Business & Stakeholder Management

Work directly with business users, data consumers, and technical teams to understand and clarify requirements.
Translate business requirements into technical specifications and data models.
Understand business processes and determine appropriate data transformation and architecture approaches.
Communicate technical solutions effectively to both technical and non-technical stakeholders.

Data Governance & Quality

Implement data quality and validation frameworks.
Follow data governance, naming conventions, taxonomy, metadata, and lineage standards.
Ensure data accuracy, consistency, traceability, and auditability.
Support enterprise data management and governance initiatives.

Key Skills – Must Have: