The candidate will have responsibilities across the following functions: ETL/ELT Pipeline Architecture and Development:

- Architect, design, and develop robust, scalable ETL/ELT pipelines for ingesting, transforming, and loading high-volume data from diverse sources, including relational databases, REST APIs, streaming platforms, flat files, and cloud storage.
- Lead the end-to-end design of data flows from raw ingestion layers through to curated, consumption-ready data products across data warehouse, data lake, and lakehouse architectures.
- Build and maintain pipelines using industry-standard frameworks, including Apache Spark, Apache Airflow, dbt, AWS Glue, Azure Data Factory, or Google Dataflow.
- Implement incremental, change data capture (CDC), and real-time streaming ingestion patterns using tools such as Apache Kafka or AWS Kinesis, where applicable.
- Define and enforce pipeline reliability standards, SLA monitoring, alerting, retry logic, idempotency, and graceful failure handling across all data engineering workloads.
- Own pipeline performance tuning, query optimisation, partitioning strategies, caching, and resource management to ensure efficient processing of large-scale datasets.

Data Modelling and Warehouse Design:

- Lead the design and implementation of dimensional and relational data models, including star schema, snowflake schema, and medallion (Bronze/Silver/Gold) architecture for data warehouse and lakehouse environments.
- Define physical data models, partitioning schemes, and storage strategies optimised for query performance and cost efficiency across cloud platforms.
- Collaborate with data architects to evolve the enterprise data model, ensuring alignment with business domain definitions and downstream consumption requirements.
- Document and maintain entity-relationship diagrams (ERDs), data dictionaries, and schema version history for all data models under ownership.
- Drive adoption of data modelling best practices and standards across the engineering team. Data Quality, Governance and Lineage:
- Design and implement comprehensive data quality frameworks, including validation rules, reconciliation checks, anomaly detection, and automated alerting - to ensure data reliability at every layer.
- Establish and maintain end-to-end data lineage tracking to provide full visibility of data origin, transformation, and movement across systems.
- Lead metadata management and data cataloguing initiatives, ensuring all datasets are well-documented, discoverable, and governed in line with enterprise data standards.
- Define and enforce data retention, archiving, and access control policies in collaboration with data governance and security teams.
- Champion DataOps practices CI/CD for data pipelines, automated testing, version control, and environment management to improve pipeline reliability and deployment velocity.

Cloud Infrastructure and Platform Engineering:

- Design and manage scalable data infrastructure on cloud platforms AWS (S3 Redshift, Glue, EMR, Kinesis), GCP (BigQuery, Dataflow, Pub/Sub), or Azure (Synapse, Data Factory, ADLS) based on project requirements.
- Work with DevOps and cloud infrastructure teams on infrastructure-as-code (Terraform, CloudFormation) for provisioning and managing data platform components.
- Optimise cloud data platform costs through right-sizing, storage tiering, query optimisation,



and workload management strategies.
- Evaluate and introduce new tools, frameworks, and cloud-native services to improve pipeline efficiency, scalability, and maintainability.
- Ensure all data infrastructure meets security, compliance, and data residency requirements applicable to the organisation and its clients.

Technical Leadership and Mentoring

- Serve as a technical lead for the data engineering team, providing architectural guidance, conducting design reviews, and setting coding and documentation standards.
- Mentor and coach junior and mid-level data engineers through hands-on guidance, code reviews, and structured feedback to accelerate their technical growth.
- Lead technical discussions, architecture design sessions, and proof-of-concept evaluations for new data engineering initiatives.
- Contribute to engineering roadmap planning, sprint estimation, and technical backlog prioritisation in collaboration with the engineering manager.
- EngiNeo Solutions: Drive adoption of best practices in pipeline testing, version control, peer review, and automated quality checks across the team.

Collaboration and Stakeholder Engagement:

- Partner closely with data architects, data scientists, ML engineers, BI developers, and business analysts to deliver high-quality, consumption-ready data products.
- Engage directly with business stakeholders to understand data requirements, translate them into technical specifications, and manage expectations on delivery timelines.
- Collaborate with data governance, security, and compliance teams to ensure all data engineering deliverables meet organisational policy requirements.
- Participate in Agile ceremonies, sprint planning, standups, retrospectives, and reviews and contribute to continuous improvement of the team's delivery processes. The core requirements for the job include the following: Education and Technical Skills:
- Bachelor's or Master's degree in Computer Science, Information Technology, Electronics, or a related engineering discipline.
- 7-10 years of hands-on experience in data engineering, with a strong focus on ETL/ELT pipeline development and data warehouse or lakehouse architecture.
- Expert-level proficiency in Python and SQL, including advanced query writing, window functions, query optimisation, and stored procedures.
- Deep, production-grade experience with at least two of: Apache Spark, Apache Airflow, dbt, AWS Glue, Azure Data Factory, Google Dataflow, or equivalent pipeline frameworks.
- Proven experience designing and implementing dimensional data models and warehouse architectures for large-scale analytical workloads.
- Hands-on experience with at least one major cloud data platform: AWS (Redshift, S3 Glue, EMR), GCP (BigQuery, Dataflow), or Azure (Synapse Analytics, ADLS, ADF).
- Strong understanding of data quality, data lineage, metadata management, and data governance principles.

Good to Have

- Experience with modern table formats and lakehouse technologies, such as Delta Lake, Apache Iceberg,



or Apache Hudi.
- Familiarity with real-time and streaming data ingestion using Apache Kafka, Apache Flink, AWS Kinesis, or Google Pub/Sub.
- Exposure to infrastructure-as-code tools such as Terraform or CloudFormation for data platform provisioning.
- Knowledge of data ontology concepts, knowledge graphs, or graph databases (Neo4j, Amazon Neptune).
- Experience with DataOps practices, CI/CD pipelines for data, automated pipeline testing frameworks, and Git-based workflow management.
- Familiarity with data observability tools such as Monte Carlo, Great Expectations, or Soda.
- Cloud certifications in AWS, GCP, or Azure data or analytics specialisations.

Soft Skills and Leadership Qualities:

- Strong ownership mindset takes end-to-end accountability for data engineering deliverables and pipeline reliability.
- Ability to communicate complex technical concepts clearly to both technical peers and non-technical business stakeholders.
- Collaborative and team-oriented, works effectively across data, analytics, ML, and business teams in a fast-paced environment.
- Structured problem-solver who can diagnose and resolve complex data pipeline issues under time pressure.
- Proactive learner who stays current with evolving data engineering tools, frameworks, and best practices.
- Experience working in Agile delivery environments with a continuous improvement mindset.

Company Summary

EngiNeo specializes in SaaS, PaaS, and IaaS implementation services, offering cutting-edge cloud solutions to catalyze digital transformation in Indian enterprises. EngiNeo Team is founded by a team of professionals of Ex-Pwc, Ex-EY, Ex-KPMG, Ex- Oracle who have a combined experience of 50+ years in Technology Consulting, driving ERP implementations, and managed services for customers across industries. We aspire to be the premier suite of cloud product services, targeting the intricate digital transformation requirements of small and medium enterprises in India.

We at EngiNeo carry expertise in driving ERP implementations and managed services for customers across industries like banking and finance, telecom, BPO, and professional services for modules like project-task management, contract management, payables, receivables, asset management, taxation, and people management. Our experience with Big4 consulting (PwC, EY, KPMG) has also enabled us to understand problems and advise customers on how to operationalize and automate their cloud investments through multiple models i. e. Applications, Infrastructure, and Services.

Required Skills

['Data Streaming', 'Data Warehousing', 'ETL', 'Kafka', 'SQL', 'Amazon Redshift', 'AWS Glue', 'Azure Data Factory', 'Azure Synapse', 'BigQuery', 'Data Analysis', 'Flink', 'Snowflake', 'Spark']

Additional Information

Source recruiter: Perfect Ventures Recruitment Source recruiter designation: Recruiter Company tagline: The cloud transformation partner Employee count: 10 Company founded: 2023 Company tech stack: NetSuite, Oracle E-Business Suite, Odoo Website: https://engineosol.com/ LinkedIn: https://www.linkedin.com/company/engineo-solutions/?originalSubdomain=in Instahyre score: NA Solid match: NA Accept outstation: true Company tagline: The cloud transformation partner Logo: https://media.instahyre.com/images/profile/base/employer/49404/e7c9d492a2/engineo_solutions_logo.webp

📌 Data Engineer (Gurugram)
🏢 HuntingCube
📍 Gurugram