Lead Data Engineer

Company: Solutionara Private Limited

Location: Noida, India – Onsite

Employment Type: Full-Time

Experience: 8–12 Years

About Solutionara Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications.

Headquartered in the United States with a Global Delivery Center (GDC) in Noida, India, Solutionara partners with enterprise organizations across retail, manufacturing, distribution, consumer goods, and other industries to design, implement, and support scalable technology solutions.

As a rapidly growing consulting organization, we foster a culture of ownership, innovation, collaboration, and continuous improvement, empowering our teams to contribute beyond their immediate responsibilities while delivering exceptional value to our global clients.

Every team member is expected to think like a consultant and business problem solver— understanding client objectives, challenging assumptions when appropriate, leveraging AI responsibly, and continuously identifying opportunities to improve quality, productivity, and customer outcomes.

AI is embedded into how we work, but it is an enabler—not a replacement for expertise, critical thinking, or accountability.

Role Overview Solutionara is seeking an experienced Lead Data Engineer to provide hands-on technical leadership across enterprise data engineering engagements.

This role is designed for a senior engineer who can operate beyond traditional pipeline development—translating business and data requirements into scalable technical solutions, defining data models and integration patterns, guiding engineering teams, and working directly with client stakeholders.

The successful candidate should combine strong data engineering expertise with solution-design capability and remain actively involved in implementation.

Key Responsibilities

Technical Leadership & Solution Design

- Lead the technical design and implementation of scalable enterprise data solutions.
- Translate business requirements into data models, integration patterns, data flows, and technical designs.
- Define solution architecture across data ingestion, transformation, storage, processing, quality, consumption, and reporting layers.
- Evaluate technical options and recommend practical solutions based on scalability, performance, maintainability, security, and cost.
- Establish engineering standards, reusable patterns, coding practices, and technical guardrails.
- Conduct design and code reviews and mentor engineers across the team.
- Remain hands-on with engineering and troubleshooting rather than operating solely in an oversight role.

Data Engineering




- Design and develop scalable ETL/ELT pipelines for structured and semi-structured data. Lead complex data ingestion, transformation, migration, and integration initiatives.
- Develop solutions using SQL, Python, APIs, batch processing, and cloud-native data services.
- Design canonical and dimensional data models appropriate to business requirements.
- Support integration of data from ERP, CRM, PLM, PIM, databases, APIs, files, and other enterprise systems.
- Establish reusable frameworks for pipeline development, validation, monitoring, and exception handling.
- Drive performance optimization and engineering reliability.

Data Quality & Governance
- Define technical approaches for data profiling, cleansing, standardization, validation, deduplication, and reconciliation.
- Establish automated data-quality controls within engineering pipelines.
- Support data lineage, metadata, reference-data, and governance requirements.
- Work with business and technical stakeholders to translate business rules into enforceable data-quality rules.
- Ensure traceability and reconciliation between source and target systems.

Client Consulting & Delivery
- Participate in client discovery sessions and technical workshops.
- Understand business objectives before recommending technical solutions.
- Communicate complex technical concepts clearly to technical and non-technical stakeholders.
- Identify risks, dependencies, assumptions, and data issues early and recommend mitigation strategies.
- Work closely with project/program leadership to support estimation, planning, delivery, and technical decision-making.
- Challenge requirements constructively where a better technical or business outcome is possible.
- Take ownership of technical outcomes through implementation and production readiness.

AI-Led Engineering Use AI-assisted engineering tools responsibly to accelerate development, analysis, documentation, testing, and troubleshooting.

Identify opportunities to automate repetitive engineering activities and improve developer productivity.

Validate AI-generated outputs for accuracy, security, maintainability, and alignment with engineering standards.

Help establish effective AI-assisted development practices within the data engineering team.

Required Qualifications

- 8–12 years of overall experience in data engineering, data platforms, data integration, or related areas.
- Solid hands-on experience with SQL and Python.
- Strong experience designing and implementing ETL/ELT pipelines.




- Experience designing enterprise data models and data integration solutions.
- Strong understanding of relational databases, data warehouses, and modern cloud data platforms.
- Experience with at least one major cloud platform such as AWS or Azure.
- Experience with technologies such as Databricks, Snowflake, Redshift, Synapse, AWS Glue, Azure Data Factory, Airflow, dbt, or equivalent.
- Strong understanding of data quality, reconciliation, lineage, and validation.Experience integrating multiple enterprise data sources.
- Demonstrated experience leading technical design and mentoring engineers.Ability to independently lead technical discussions with client stakeholders.
- Strong analytical, troubleshooting, communication, and documentation skills.

Preferred Qualifications
- Experience supporting enterprise clients in manufacturing, retail, distribution, or consumer goods.
- Experience working with data from ERP, CRM, PLM, PIM, MES, or similar enterprise applications.
- Experience with product, material, supplier/vendor, customer, location, hierarchy, or reference data.
- Experience with API-based and event-driven integration. Experience with CI/CD and DevOps practices for data engineering.
- Prior experience working in an IT consulting or technology services environment. What Success Looks Like
- Technical solutions are scalable, maintainable, and aligned with client objectives.
- Engineering teams have clear designs, standards, and technical direction.
- Data pipelines are reliable, observable, and supported by effective quality controls.
- Technical risks and data issues are identified before they become delivery problems.
- Client stakeholders view you as a trusted technical problem solver, not simply an implementation resource.
- AI and automation are used thoughtfully to improve engineering productivity without compromising quality or accountability.

Pay: ₹2,000,000.00 - ₹2,600,000.00 per year

Benefits

- Cell phone reimbursement
- Internet reimbursement
- Paid sick time
- Paid time off
- Provident Fund

Ability to commute/relocate:
- Noida, Uttar Pradesh (Noida): Reliably commute or planning to relocate before starting work (Required)

Application Question(s):
- Which ETL/ELT tools have you used in production? (Select all that apply)

Informatica

Talend dbt

Azure Data Factory

AWS Glue

Custom pipelines (Python/SQL)

None

- Describe one data migration project you worked on. What was your role and what tools/technologies did you use?
- Which data platforms have you worked with? (Select all that apply)

Snowflake

Databricks

Redshift

BigQuery

Traditional RDBMS (Oracle, SQL Server, MySQL)

None

- Have you used AI tools or automation in your data engineering work? What tools have you used?

Work Location: In person

📌 Lead Data Engineer (Noida)
🏢 Solutionara
📍 Noida