Data Engineer (Databricks)

Onit; Pune District; On-site; INR 1,200,000 - 2,100,000; Full time

Benefits offered by this job

Health Coverage

Flexible paid time off

Family leave and caregiver support

Life and disability insurance

Voluntary benefits

Tax-advantaged accounts

Volunteer day

Job summary

Onit is expanding its AI-driven legal operations platform and seeks a Data Engineer to design and operate Databricks-based data pipelines powering benchmarking capabilities. You will integrate data from multiple enterprise sources, ensure data quality, and enable scalable analytics for multi-tenant environments.

You will work with Data Architect, Data Scientist, and Product teams to deliver reliable ingestion, entity resolution, and governance across the data stack, with privacy-conscious

Qualifications

• Minimum 5 years of data engineering or analytics engineering experience.
• Hands-on Databricks with PySpark and Spark SQL.
• Strong SQL and Python skills with data modeling expertise.
• Experience with deduplication, entity resolution, and data quality problems at scale.
• Experience with cloud platforms (AWS preferred) and CI/CD for data pipelines.

Responsibilities

• Design, build, and maintain scalable batch and incremental pipelines in Databricks (PySpark, Spark SQL, Delta Lake).
• Implement canonical data model with Data Architect for consistent reconciliation across product surfaces.
• Build production pipelines for duplicate/conflicting records across systems.
• Profile source data, implement automated data quality checks, and monitor data quality against targets.
• Orchestrate and monitor jobs with Databricks Workflows; optimize Spark performance and cost.
• Apply Unity Catalog governance and multi-tenant data isolation for analytics.
• Collaborate with Data Scientist and Engineering to expose curated data to the product.

Skills

Databricks

PySpark

Spark SQL

Delta Lake

Unity Catalog

Python

SQL

Cloud AWS

CI/CD

Data modeling

Tools

Databricks Workflows

Git

CI/CD

Delta Live Tables

Job description

About Onit

We're redefining the future of legal operations through the power of AI. Our cutting-edge platform streamlines enterprise legal management, matter management, spend management and contract lifecycle processes, transforming manual workflows into intelligent, automated solutions. We’re a team of innovators using AI at the core to help legal departments become faster, smarter, and more strategic. As we continue to grow and expand the capabilities of our new AI-centric platform, we’re looking for bold thinkers and builders who are excited to shape the next chapter of legal tech. If you're energized by meaningful work, love solving complex problems, and want to help modernize how legal teams operate, we’d love to meet you.

Position Summary

The Data Engineer builds and operates the Databricks-based data pipelines that power a new customer-facing benchmarking capability in Onit's AI-driven legal operations platform. Integrating data from multiple source systems across enterprise legal management, matter management, and spend management, this role turns inconsistent, duplicated, and incomplete source data into a clean, reconciled, and trustworthy foundation for analytics. Working closely with the Data Architect, Data Scientist, and Product, the Data Engineer delivers reliable ingestion, entity resolution, and data quality measurement at scale, with multi-tenant data isolation and privacy built in.

Key Responsibilities

• Pipeline Development: Design, build, and maintain scalable batch and incremental pipelines in Databricks (PySpark, Spark SQL, Delta Lake) that ingest data from multiple source systems into a medallion (bronze/silver/gold) architecture.
• Data Integration & Modelling: Implement the canonical data model defined with the Data Architect, so figures reconcile consistently across every product surface.
• Entity Resolution: Build production pipelines that resolve duplicate and conflicting records across systems that were never designed to reconcile with one another.
• Data Quality Engineering: Profile source data, implement automated data quality checks and expectations, and build monitoring and reporting so data quality stays continuously visible against defined targets.
• Orchestration & Reliability: Orchestrate and monitor jobs with Databricks Workflows, optimise Spark performance and cost, and troubleshoot pipeline failures.
• Governance & Privacy: Apply Unity Catalog governance, multi-tenant data isolation, and anonymisation and aggregation rules for pooled cross-customer analytics.
• Collaboration: Partner with the Data Scientist to productionise comparability models and data quality metrics, and with Engineering to expose curated data to the product.

Required Skills

• 5+ years in data engineering or analytics engineering, building production data pipelines in a cloud environment.
• Hands-on Databricks experience: PySpark and Spark SQL, Delta Lake, Databricks Workflows/Jobs, and Unity Catalog.
• Strong SQL and Python, with solid data modelling skills (dimensional and/or canonical models) and experience with the lakehouse/medallion architecture.
• Practical experience with deduplication, record linkage, or entity resolution, and with diagnosing and remediating data quality problems at scale.
• Experience implementing automated data quality checks, pipeline monitoring, and Spark performance and cost optimisation.
• Experience with cloud platforms (AWS preferred), Git-based version control, and CI/CD for data pipelines.
• Clear communication with data scientists, architects, and product stakeholders, including flagging data limitations early.
• Working proficiency with AI tools (e.g., Claude, Databricks Assistant, GitHub Copilot) to accelerate pipeline development, debugging, and documentation, with the judgment to validate AI-generated code before production use.

Preferred Qualifications

• Databricks certification (e.g., Data Engineer Associate or Professional).
• Experience with streaming or incremental ingestion (e.g., Auto Loader, Structured Streaming, Delta Live Tables).
• Experience building data for benchmarks, scoring models, or analytics intended for external consumption.
• Experience with anonymisation, aggregation thresholds, or privacy-preserving analytics.
• Background in enterprise B2B SaaS or the legal technology domain.

Benefits & Perks That Support You

• Health Coverage: Employee and immediate family members.
• Time Away: Flexible paid time off and 10 company paid holidays annually.
• Family Support: Exceptional paid leave for birth parents, non-birth parents, and caregivers. Onit also offers surrogacy and adoption reimbursement.
• Income Protection: 100% employer-paid life and disability insurance.
• Additional Coverage Options: Voluntary benefits including hospital indemnity, critical illness, accident.
• Tax-Advantaged Accounts: Flexi, NPS.
• Community Engagement: One paid volunteer day each year to give back to the community.

Our Commitment to Applicants

We know that not everyone will check every box in a job description. At Onit, we value diversity, inclusion, and authenticity. If you're excited about this role but your experience doesn't align perfectly with every qualification, we encourage you to apply. You may be exactly who we're looking for.

Onit Values

Customer First - Customer success is our success. We deliver value, listen, and act on customer needs.

Purposeful Innovation - Innovation fuels our growth. We harness creativity to solve problems and lead with the intentions and expertise.

Win as One - Teamwork is how we win. We are accountable, act with integrity, and communicate openly.

Intentional Growth - Our people are the difference. We create an environment with compelling work, impactful contributions, and career growth.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.