Momentum Services
Website:
momentummetropolitanservices.co.in
Company:
https://www.linkedin.com/company/momentumservicesindia
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
Role Purpose
We are seeking a Data Engineering Led to design, build and lead scalable, secure and reliable
data platforms that enable analytics, reporting and advanced data use cases. This role provides
technical leadership across data ingestion, transformation and storage, while partnering closely
with analytics, architecture and business teams to ensure data is trusted, governed and fit for
purpose.
Requirements
- Strong data development, analysis and visualisation skills using tools (e.g. SQL, AWS,
Databricks, Power BI, Qlik, etc.)
- Cloud Platforms AWS, Microsoft, Snowflake or Databricks
- Strong hands-on experience with SQL and data pipeline tools
Duties and Responsibilities
Key Accountabilities / Kras / Kpis
- Lead the design, build and maintenance of robust, scalable and reusable data pipelines
for batch and near–real-time processing.
- Architect and oversee data ingestion, transformation and storage patterns across
structured and semi-structured data sources.
- Translate business and analytical requirements into performant, secure and costeffective data engineering solutions.
- Define and maintain detailed technical specifications for data sources, data flows,
transformations, storage layers and downstream consumption.
- Ensure the production and upkeep of technical artefacts including source-to-target
mappings, data models, pipeline documentation and data dictionaries.
- Collaborate with analytics, BI, architecture, governance and application teams to enable
trusted, analytics-ready data.
- Champion enterprise data engineering standards, patterns and frameworks to ensure
consistency, reliability and maintainability.
- Embed data quality, validation, lineage and observability into data pipelines by design.
- Ensure secure handling of live, sensitive and confidential data in line with governance,
regulatory and compliance requirements.
- Assess and manage the impact of upstream system changes on data pipelines, platforms
and downstream consumers.
- Optimise data platform performance, scalability and cost efficiency.
- Lead testing, monitoring, troubleshooting and incident resolution for data pipelines and
platforms.
CLIENT
- Act as a trusted technical advisor on data engineering and platform-related matters.
- Build strong working relationships with business stakeholders, analytics teams,
architects and technology partners.
- Deliver data engineering capabilities in line with agreed service levels and platform
expectations.
- Provide recommendations to improve data availability, reliability and time-to-insight for
internal and external consumers.
- Contribute to a culture of collaboration, transparency and high-quality delivery across
the data ecosystem.
PEOPLE
- Lead, coach and mentor data engineers, fostering a high-performance engineering
culture.
- Promote engineering excellence, DevOps and automation practices across the team.
- Encourage continuous learning in data engineering tools, cloud platforms and emerging
technologies.
- Positively influence and participate in change initiatives across the data and technology
landscape.
- Contribute to innovation through the introduction of new tools, frameworks and
delivery approaches.
- Take ownership of personal leadership development and succession planning within the
data engineering capability.
FINANCE
- Identify opportunities to improve cost efficiency and performance of data platforms and
pipelines.
- Manage data engineering resources and platform costs responsibly.
- Contribute to risk identification and mitigation related to data availability, integrity,
security and compliance.
- Support the enablement of reporting, dashboards, analytics and advanced use cases
through well-designed data foundations.
- Communicate delivery progress, risks and technical decisions clearly to technical and
non-technical stakeholders.
- Proactively identify opportunities to improve data platform maturity, automation and
engineering efficiency.
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