1. About Flentas Technologies
Flentas Technologies is a Pune-headquartered cloud consulting and delivery company with offices in Mumbai, Hyderabad and Dubai. An AWS Advanced Consulting Partner holding the AWS Generative AI, DevOps and IoT Competencies and a 2025 AWS Partner of the Year award recipient, Flentas delivers cloud migration and modernisation, DevOps, managed services, cloud security, data engineering, IoT and Generative AI solutions to enterprises across India and the GCC through a team of more than 100 certified cloud professionals, with particular depth in banking, financial services, insurance and other regulated industries.
2. Position Summary
The Data Solutions Architect leads Flentas Technologies' Data Analytics vertical and is the senior technical owner of its data platform and analytics engagements. The role spans the full engagement lifecycle — discovery and assessment, architecture, Statement of Work authoring and pricing, and accountable ownership through delivery — for data lakehouse, streaming analytics, data migration, data governance, DataOps and business intelligence programmes on AWS.
The role also owns the vertical's offerings catalogue, Data Strategy collateral, reference architectures, estimation standards and team enablement, and provides the governed data foundation on which Flentas's Generative AI solutions are built, working closely with the AI Solutions Architect. Approximately half of the role is presales and solution design; the remainder is architecture ownership during delivery, vertical leadership and team development.
3. Key Responsibilities
3.1 Presales and Solution Design
- Lead technical discovery and assessments for data opportunities: current-state reviews of data estates, data strategy and roadmap engagements, migration assessments (warehouse, database and BI estates to AWS; on-premises virtualization inventories) and platform audit and optimization reviews.
- Author Statements of Work, proposals and assessment reports; define team composition and phases (Discover, Proof of Concept, Scale, Support); prepare AWS Pricing Calculator estimates and total-cost-of-ownership models (for example legacy analytics platform exit economics, Amazon Redshift versus Athena, storage tiering and retention).
- Produce architecture diagrams, reference architectures and case-study material; represent Flentas technically with AWS account teams and in partner co-sell activity.
3.2 Architecture and Delivery Ownership
- Design modern data platforms on AWS: S3 data lakes and lakehouses (Parquet, Apache Iceberg, S3 Tables), AWS Glue (Data Catalog, ETL / PySpark), Amazon EMR, Athena, Redshift (Serverless and Spectrum), OpenSearch, Lake Formation and Amazon DataZone / SageMaker Catalog.
- Design streaming and near-real-time pipelines: Kinesis Data Streams and Firehose, Amazon MSK, Managed Service for Apache Flink and Spark Structured Streaming; event-driven filter, aggregate, enrich and route patterns with SQS and EventBridge.
- Own orchestration and DataOps: MWAA (Airflow), Step Functions and EventBridge; CI/CD and infrastructure as code (Terraform or CDK); data quality (AWS Glue Data Quality, Deequ, Great Expectations); observability and lineage; and cost governance (partition design, file compaction, storage tiering, online versus archival retention).
- Lead migrations: AWS DMS and SCT, on-premises warehouse and BI estates to AWS, packaged analytics products to custom pipelines, and legacy analytics platform exits.
- Design BI and consumption layers: Amazon QuickSight (embedded analytics, row-level security), semantic and KPI layers, and self-service enablement for business users.
- Build security and governance in by design: IAM, KMS, VPC, Lake Formation permissions, Amazon Macie, encryption at rest and in transit, PII handling, DPDP and regional data residency; security-data-lake patterns for security-analytics use cases.
- Provide the data foundation for AI: curated and de-identified corpora for RAG, metadata and feature layers, vector-ready pipelines and data-quality gates, in collaboration with the AI Solutions Architect.
- Act as the accountable architect through delivery: technical lead for data teams (data engineers, BI developers and QA), design reviews, milestone gates, acceptance, production handover and managed-services runbooks.
3.3 Vertical Leadership and AWS Partnership
- Lead the Data Analytics vertical: own the offerings catalogue (lakehouse design and build, streaming analytics, data migration, pipeline engineering, data governance, BI and insights, DataOps and observability, platform review and optimization), the Data Strategy deck, standard reference architectures and estimation templates.
- Develop the team: own the AWS data-analytics training module and certification tracker (AWS Certified Data Engineer – Associate roadmap), interview and hire data engineers, and mentor team leads.
- Drive AWS partner programmes for data: ACE opportunity registration, migration, assessment and proof-of-concept funding, Well-Architected reviews for data workloads, and evidence toward AWS data and analytics designations.
- Contribute reference architectures, presales accelerators and documentation to the Cloud Center of Excellence (Confluence).
3.4 Thought Leadership
- Represent Flentas externally through AWS Community Day sessions, technical blogs, customer webinars and partner events; publish reference architectures and success stories.
4. Required Qualifications and Experience
- Bachelor's degree in Computer Science, Engineering or a related discipline.
- 8–12 years of experience in data engineering and analytics, including a minimum of 4 years architecting AWS data platforms and 2 years in a customer-facing architect or lead role.
- Demonstrated presales experience: discovery and assessment, SOW and proposal authoring, estimation and pricing, and executive and technical presentations.
- Demonstrated leadership of a data team, vertical or multiple concurrent delivery teams, including mentoring and hiring.
- Migration experience: AWS DMS and SCT, on-premises warehouse or BI estates to AWS, and legacy platform exits.
- Excellent written and verbal English; assessment reports, SOWs and client correspondence are core outputs of the role.
5. Required Technical Skills
- AWS data stack: S3 (Parquet, Iceberg, S3 Tables), Glue, Athena, Redshift, EMR / Spark, Kinesis or MSK, Lambda, Step Functions / MWAA, Lake Formation, Glue Data Catalog, QuickSight and OpenSearch; IAM, KMS and VPC fundamentals; Terraform or CDK.
- Strong SQL and Python / PySpark; data modelling (dimensional, data vault, lakehouse medallion); partitioning and performance tuning; cost optimisation at terabyte-to-petabyte scale.
- Streaming and near-real-time architecture (detection, aggregation and enrichment pipelines).
- Governance and security: PII handling, row-level security, catalog and lineage, retention policy, DPDP and regional data residency.
6. Preferred Skills
- Security-analytics or SIEM data platforms (OCSF, Amazon Security Lake); Splunk or Vertica exits.
- GenAI-adjacent data work: RAG corpora, embedding pipelines, Bedrock natural-language-to-SQL, Textract or Comprehend document pipelines.
- Snowflake, dbt, change-data-capture tooling (Debezium, Kafka Connect); IoT and telemetry ingestion.
- Familiarity with BI tools customers migrate from (Power BI, Tableau) into QuickSight.
- AWS partner-ecosystem experience: ACE, funding programmes, competency or service-delivery submissions.
- Gaming, payments, BFSI or GCC customer exposure.
7. Certifications (Good to Have)
Certifications are desirable but not mandatory; demonstrated hands-on delivery experience carries greater weight in selection.
- AWS Certified Solutions Architect – Associate or Professional.
- AWS Certified Data Engineer – Associate (or the retired AWS Certified Data Analytics – Specialty).
8. Key Result Areas (First 12 Months)
- All data opportunities qualified with an assessment, SOW and estimate within agreed presales turnaround times.
- At least two data platforms taken from discovery to production with acceptance criteria met and handover to managed services.
- Offerings catalogue and Data Strategy deck refreshed and adopted by sales; reference architectures and estimation templates published in Confluence.
- Team certification uplift (AWS Certified Data Engineer – Associate) tracked and delivered.
- ACE registrations and AWS migration or assessment funding applied to qualifying opportunities; Well-Architected data reviews completed for key accounts.
9. Key Working Relationships
- Internal: Chief Technology Officer and co-founders, AI Solutions Architect, Senior Solutions Architects, account managers, data engineering team leads and engineers.
- External: AWS partner-development and account teams; AWS Professional Services on large joint engagements; client CXOs, heads of data and compliance stakeholders in India and the GCC.
10. Professional Competencies
- Consultative, outcome-oriented engagement with senior client stakeholders.
- Commercial acumen: the ability to scope, estimate, price and defend an engagement.
- Ownership and accountability across the presales-to-delivery lifecycle.
- Structured written communication and documentation discipline.
- Practice leadership: building capability, standards and a pipeline of talent within the vertical.
