4 – 7 Years – ennVee | Careers https://careers.ennvee.com Fri, 24 Apr 2026 06:47:58 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://careers.ennvee.com/wp-content/uploads/2024/09/cropped-ennvee-favicon-32x32.webp 4 – 7 Years – ennVee | Careers https://careers.ennvee.com 32 32 Data Engineer https://careers.ennvee.com/jobs/data-engineer/ Thu, 23 Apr 2026 17:02:30 +0000 https://careers.ennvee.com/?post_type=awsm_job_openings&p=33443

Data Engineer

  • 4 - 7 years of experience
  • Bengaluru, Chennai
  • Full Time
We are building out our Data Intelligence practice and are seeking a Data Engineer with 3–5 years of hands-on experience delivering solutions on a modern cloud data platform. The Data Engineer will design, develop, and maintain data pipelines and platform components that power analytics, reporting, and AI/ML use cases across the organization. The role is focused on building robust, scalable, and secure data solutions that enable efficient data consumption and digital transformation.

The ideal candidate has production experience on Databricks or Snowflake (Databricks preferred) and is comfortable working across data modeling, pipeline development, data quality, and governance. The Data Engineer will partner closely with analysts, data scientists, and business stakeholders, and will be expected to own assigned workstreams from concept through delivery. Employee may perform other related duties as required to meet the ongoing needs of the organization.

Essential Responsibilities
  • Build, enhance, and maintain production data pipelines and datasets on a modern cloud data platform (Databricks or Snowflake), with an emphasis on stability, reliability, and continuous improvement.
  • Develop efficient ingestion, transformation, and curation workflows using industry-standard patterns such as the medallion architecture (bronze / silver / gold) or an equivalent layered design.
  • Design and implement dimensional and analytical data models (Kimball star schema, Data Vault, or equivalent) that support reporting, self-service analytics, and downstream AI/ML workloads.
  • Troubleshoot and resolve data pipeline, data quality, and platform issues promptly, with clear root-cause analysis and durable fixes.
  • Partner with stakeholders across the organization to understand data needs, translate requirements into technical designs, and set clear expectations on scope and delivery.
  • Contribute to data security and governance — including access controls, PII handling, row-level security, masking, and usage logging — using tools such as Unity Catalog, Snowflake Horizon, or equivalent.
  • Implement data quality checks and observability (expectations, tests, monitoring, alerting) to ensure trustworthy datasets for downstream consumers.
  • Support analysts and report builders with dataset design, documentation, and best practices for modern BI tools (Power BI, Tableau, Looker, or similar).
  • Participate in code reviews, CI/CD deployments, and change management; own the quality of your releases to production.
  • Stay current with platform features and recommend adoption of new capabilities where they drive measurable value.
Required Qualifications
  • 3–5 years of hands-on experience in a data engineering or closely related technical role.
  • Production experience delivering solutions on a modern cloud data platform — Databricks or Snowflake (Databricks strongly preferred).
  • Strong proficiency in SQL and Python, including writing performant, well-tested, production-grade code.
  • Hands-on experience building ETL/ELT pipelines — ingestion, transformation, cleansing, and curation — against large, complex datasets.
  • Working knowledge of data modeling techniques (Kimball / dimensional modeling, Data Vault, or medallion architecture) and when to apply each.
  • Experience with workflow orchestration tools such as Apache Airflow, Azure Data Factory, Databricks Workflows, dbt, or equivalent.
  • Experience integrating with enterprise source systems — ERPs (e.g., SAP, Oracle, Dynamics, Workday), CRMs, APIs, and relational databases.
  • Hands-on experience with at least one major cloud provider (Azure, AWS, or GCP); Azure preferred.
  • Experience with Git-based version control and CI/CD for data pipelines (Azure DevOps, GitHub Actions, GitLab CI, or similar).
  • Exposure to data quality and observability practices — test frameworks, expectations, lineage, monitoring, and alerting (Great Expectations, dbt tests, Monte Carlo, or similar).
  • Familiarity with Agile/Scrum delivery and collaborative development environments.
  • Bachelor’s degree in computer science, Engineering, a STEM field, or equivalent practical experience.
Preferred Qualifications
  • Production experience with Databricks Unity Catalog, Delta Lake, and Delta Live Tables; or Snowflake equivalents (Horizon, Dynamic Tables, Streams & Tasks).
  • Experience with streaming / real-time data pipelines (Kafka, Event Hubs, Kinesis, Structured Streaming, Snowpipe Streaming) and/or IoT data patterns.
  • Working knowledge of Machine Learning (ML), Large Language Models (LLMs), and common AI/ML data enablement patterns (feature stores, vector stores, RAG).
  • Experience managing platform cost and performance — cluster/warehouse sizing, cost reporting, budgets, and alerting.
  • Experience administering a modern BI platform (Power BI, Tableau, Looker) — workspace governance, certified datasets, and best-practice enforcement.
  • Experience with Infrastructure as Code (Terraform, Bicep).
  • Experience with R, Scala, or other statistical / JVM-based programming languages.

Preferred Certifications

  • Databricks Certified Data Engineer Associate or Professional
  • SnowPro Core / SnowPro Advanced: Data Engineer
  • Microsoft Certified: Azure Data Engineer Associate
  • AWS Certified Data Engineer – Associate, or Google Cloud Professional Data Engineer
Soft Skills & Ways of Working
  • Strong written and verbal communication — able to explain technical concepts clearly to both technical and non-technical audiences.
  • Stakeholder management — comfortable gathering requirements, negotiating scope, and providing transparent status updates.
  • Documentation-first mindset — takes ownership of producing clear technical documentation, runbooks, and data dictionaries.
  • Collaborative and curious — works effectively in cross-functional teams (analysts, data scientists, product, business SMEs) and invests in teammates’ success.
  • Ownership and accountability — independently manages assigned workstreams end-to-end, flags risks early, and follows through on commitments.
  • Pragmatic problem solver — balances long-term architectural quality with the need to deliver incremental business value.
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