Data Engineer

AssetMark Global Wealth; Hyderabad; Hybrid; INR 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Comprehensive benefits

Hybrid work model

Job summary

AssetMark Global Wealth seeks a Data Engineer for its Billing team in Hyderabad. You will design, build, and operate Azure/Databricks ETL/ELT pipelines, curate billing-ready data, and ensure data quality and timely delivery across data hubs.

The role emphasizes secure handling of financial data, strong collaboration with cross‑functional teams, and production reliability. You will work on API and file-based integrations, with ActiveBatch scheduling, and contribute to future Snowflake adoption in

Qualifications

• 3-6 years of hands-on data engineering experience with production pipelines.
• Strong Python/PySpark, SQL, and ETL/ELT design capabilities.
• Experience with Azure-based data platforms and data-hub concepts.
• Experience with data quality checks, reconciliation, and secure handling.

Responsibilities

• Design, build, test, and maintain ETL/ELT pipelines in Azure and Databricks.
• Develop and curate trusted billing-ready data assets and datasets.
• Implement automated data quality validations and reconciliation controls.
• Manage orchestration with ActiveBatch and ensure on-time processing within windows.
• Build integrations via APIs, databases, and file transfers with robust error handling.
• Monitor pipelines, investigate failures, and apply safe in-window fixes.
• Collaborate with Billing, Operations, Product, and data teams to meet requirements.
• Adhere to secure data handling practices for sensitive financial data.

Skills

Python scripting

PySpark

SQL

ETL/ELT design

Data quality checks

Problem solving under time pressure

Communication skills

Education

Bachelor's degree in CS/IS/Engineering

Tools

Databricks

Azure

ActiveBatch

Snowflake

APIs

Git / CI-CD

Job description

AssetMark is a leading wealth management platform dedicated to empowering financial advisors and the investors they serve. As we continue to expand our global capabilities, we are establishing a Global Capability Center (GCC) in Hyderabad to enhance our operational excellence, strengthen strategic capabilities, and support our long‑term growth.

We are seeking an experienced Data Engineer to join the Billing team in Hyderabad. Billing is a business‑critical and highly sensitive area where data accuracy, reliability, traceability, and on‑time delivery are essential. You will build and operate data pipelines, curated data assets, and integrations that support time‑sensitive billing processes and downstream consumers.

About The Opportunity

You will work primarily in Microsoft Azure with Databricks, Python, PySpark, SQL, and ETL/ELT patterns, using ActiveBatch for orchestration and scheduling. The role includes integrations with internal systems and third‑party providers through APIs, databases, and file‑based interfaces, as well as ownership of data hub and curation processes that must be complete, accurate, and delivered within narrow processing windows.

The Billing team operates with a high quality bar and limited tolerance for delay. When production issues occur, the engineer is expected to investigate quickly, communicate clearly, implement safe corrective actions within the active processing window, and propose practical alternatives when the original approach is blocked. Strong judgment, prioritization, and time management are critical to success in this role.

Snowflake is expected to become part of the future‑state ecosystem, so prior Snowflake exposure is useful but is not a requirement for the current role.

This role is based in Hyderabad and follows a hybrid work model. You will collaborate with colleagues across India and the United States, with reasonable working‑hour overlap when needed for team ceremonies, planning, production support, and delivery.

Key Responsibilities

• Billing Data Engineering: Design, build, test, deploy, and maintain reliable ETL/ELT pipelines and reusable data components in Azure and Databricks using Python, PySpark, and SQL.
• Data Hub & Curation: Transform source data into trusted, billing‑ready curated datasets. Maintain consistent business rules, source‑to‑target traceability, and accurate delivery across data hub and curation layers.
• Data Quality & Reconciliation: Implement rigorous automated validations and reconciliations for completeness, accuracy, consistency, duplicates, referential integrity, and expected volumes. Treat data quality defects as production‑impacting issues and resolve them with urgency.
• Orchestration & Scheduling: Develop and support ActiveBatch schedules, dependencies, execution chains, reruns, and recovery procedures. Ensure pipelines complete within narrow billing windows and downstream delivery commitments.
• Third‑Party & Internal Integrations: Build and operate integrations with third‑party providers and internal systems using APIs, databases, secure file transfers, and other appropriate interfaces. Design for retries, recoverability, idempotency, and clear failure handling.
• Production Reliability & Incident Response: Monitor pipeline execution and data delivery; investigate failures, data anomalies, and missed dependencies immediately. Apply safe in‑window fixes or workarounds when necessary, communicate impact and status, and follow through with root‑cause and permanent corrective actions.
• Performance & Timeliness: Optimize jobs and workflows to meet strict processing deadlines. Identify bottlenecks early, manage competing priorities, and esc….
• Problem Solving & Alternatives: Break down ambiguous technical and data issues methodically, validate assumptions, and propose alternative implementation paths when constraints or dependencies prevent the preferred solution.
• Business & Technical Collaboration: Partner with Billing, Operations, Product, engineering, data, and other stakeholders to clarify requirements, understand business rules, communicate tradeoffs, and translate needs into dependable technical solutions.
• Security & Sensitive Data Handling: Follow secure engineering and data‑handling practices for sensitive financial and billing data, including least‑privilege access, appropriate controls, auditability, and protection of confidential information.
• Engineering Practices: Use Git‑based workflows, code reviews, automated testing, deployment standards, documentation, and maintainable design patterns. Build solutions that can be supported under production time pressure without sacrificing quality.
• Continuous Improvement: Improve pipeline reliability, operational runbooks, test coverage, observability, recovery procedures, and team knowledge sharing based on production learnings and recurring failure patterns.

Knowledge, Skills, Abilities

• Strong hands‑on experience with Databricks for production data engineering workloads.
• Strong Python and PySpark skills for data transformation, automation, validation, troubleshooting, and operational support.
• Strong SQL skills with the ability to analyze, transform, reconcile, and validate complex datasets.
• Practical experience designing, building, and supporting ETL/ELT pipelines in Microsoft Azure.
• Experience with job orchestration, scheduling, dependencies, and recovery patterns; ActiveBatch experience is preferred, or equivalent experience with enterprise batch/orchestration tools.
• Experience integrating data from third‑party providers and internal systems through APIs, databases, and file‑transfer patterns.
• Demonstrated ability to implement high‑confidence data quality checks, source‑to‑target validation, reconciliation controls, and production‑ready error handling.
• Strong understanding of curated data layers, data modeling, dependency management, schema evolution, and reliable downstream delivery.
• Ability to troubleshoot production data issues quickly, isolate root causes, make sound decisions under time pressure, and implement safe corrective actions.
• Strong time management and prioritization skills, including the ability to deliver within narrow, non‑negotiable processing windows and manage multiple urgent dependencies.
• Excellent written and verbal communication skills with the ability to provide concise status, explain technical issues to non‑technical partners, and raise risks early.
• Strong problem‑solving mindset with a willingness to challenge assumptions and propose practical alternative solutions when needed.
• Experience with version control, collaborative Git workflows, automated testing, and CI/CD practices.
• Understanding of secure handling of sensitive or regulated financial data, access controls, privacy, and auditability.
• Exposure to Snowflake is a plus for the team's future‑state evolution, but Snowflake experience is not required for the current environment.
• Experience in billing, payments, financial technology, wealth management, asset management, or another high‑control financial domain is strongly preferred.

Qualifications & Experience

• 3-6 years of hands‑on experience in data engineering, data integration, software engineering, or a closely related technical role, including production support responsibilities.
• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
• Demonstrated experience supporting business‑critical data pipelines with strict quality and delivery expectations.
• Experience working with sensitive or regulated financial data and following secure data‑handling practices is required.
• Ability to work effectively in a hybrid, globally distributed engineering organization and collaborate across technical and business teams.

Why Join AssetMark?

• Work on a business‑critical Billing platform where engineering quality, data accuracy, and reliable delivery have direct operational impact.
• Build and operate modern Azure and Databricks data solutions using Python, PySpark, SQL, ETL/ELT, ActiveBatch, and third‑party integrations.
• Help shape the team's data engineering practices and future‑state platform evolution, including planned Snowflake adoption.
• Develop deep technical and business‑domain expertise in financial services while solving complex, time‑sensitive data challenges.
• Collaborate with colleagues in India and the United States on meaningful technology that supports financial advisors and their clients.
• Access comprehensive training resources, workshops, and learning materials designed to accelerate professional development.
• Enjoy comprehensive benefits, competitive compensation, and a supportive work environment that values growth and high‑quality engineering.

Our Commitment

AssetMark values diversity of thought and background and is committed to building an inclusive workplace. We encourage applications from candidates of all backgrounds, including those who are traditionally underrepresented in technology.

Employment is subject to applicable local requirements and successful completion of AssetMark's hiring process.