Data Engineer
Experience:
2–3 years
Location:
Bengaluru (Hybrid)
About the Company
Akaike Technologies
is a fast-growing AI-first organization focused on building real-world, high-impact AI systems across industries. We work at the intersection of
Generative AI, Multimodal AI, and Large-Scale ML Engineering
, enabling enterprises to operationalize cutting-edge AI solutions at scale. We foster a culture of ownership, deep technical rigor, and continuous learning.
Experience Pre-Requisite
2–3 years of hands-on experience in Data Engineering
, with strong exposure to
Databricks (PySpark, Spark SQL)
and
Azure Data Services
(ADF, ADLS, Azure SQL). Candidates must demonstrate real-world experience in
designing, building, deploying, and optimizing scalable data pipelines
end-to-end.
Job Description
We are seeking a highly skilled
Data Engineer
to design, develop, and deploy scalable data solutions on
Databricks
and
Azure Data Services
. This role requires deep technical expertise in
PySpark
,
SQL
,
Delta Lake
, and
Medallion Architecture
, combined with strong problem-solving ability and collaboration skills.
The ideal candidate will take
end-to-end ownership
of data engineering workflows—from
data ingestion and transformation
to
data modeling and governance
—while ensuring performance, reliability, and security. You will work in a fast-paced, cloud-native environment, partnering with cross-functional teams to deliver
production-grade data pipelines
that enable advanced analytics and business insights.
Key Responsibilities
1. Design and Development
- Design, develop, and deploy scalable data pipelines using
Databricks (PySpark, Spark SQL)
,
Azure Data Factory
, and other
Azure data services
.
2. Data Processing
- Write and optimize
complex SQL queries
for data extraction, transformation, and analysis.
- Utilize
PySpark
for large-scale data processing and advanced analytics.
- Implement
data partitioning
,
bucketing
, and
indexing strategies
for efficient data retrieval.
3. Data Integration
- Integrate data from multiple sources, including
structured
,
semi-structured
, and
unstructured
formats.
- Work with
APIs
,
streaming data
, and
batch processing
to ensure seamless data integration.
4. Data Governance and Quality
- Apply
data governance practices
to maintain data quality, consistency, and security.
- Monitor and troubleshoot data pipelines to ensure
accuracy
,
availability
, and
reliability
.
5. Collaboration
- Partner with
data scientists
,
analysts
, and other stakeholders to understand requirements and deliver solutions.
- Work closely with
DevOps teams
to deploy and monitor data pipelines in production environments.
6. Documentation
- Document
data pipelines
, workflows, and processes for knowledge sharing and future reference.
- Maintain up-to-date documentation on
data architecture
and
data models
.
Must Have Technical Skills
Must Have Soft Skills
- Communication:
Ability to clearly articulate technical ideas to both technical and non-technical audiences, especially in client-facing discussions.
- Problem Solving:
Strong analytical mindset with the ability to structure ambiguous problems and deliver actionable AI solutions quickly.
- Ownership & Bias for Action:
Self-driven, accountable, and proactive in driving outcomes end to end.
- Collaboration:
Empathy, active listening, and effective conflict resolution in cross-functional teams.
Relevant to Have
- Experience with
Azure DevOps
for CI/CD pipelines.
- Knowledge of
Delta Live Tables (DLT)
in Databricks.
Benefits and Perks
- Competitive compensation and ESOPs
- Opportunity to work on cutting-edge Generative AI and multimodal systems
- High visibility across teams and leadership
- Support for continuous learning, certifications, and conference participation
