Director - Data Engineering at Searce Inc · Bengaluru (Bangalore), Mumbai, Pune · 10 - 18 years · Profitable · Posted 25 Jun 2026

Director - Data engineering
What are we looking for
real solver?
Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious & the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.
Your Responsibilities
what you will wake up to solve.
1. Delivery & Tactical Rigor
- Methodology Implementation: Implement and manage a unified, 'DataOps-First' methodology for data engineering delivery (ETL/ELT pipelines, Data Modeling, MLOps, Data Governance) within assigned business units. This ensures predictable outcomes and trusted data integrity by reducing architecture variability at the project level.
- Operational Stewardship: Drive initiatives to optimize team utilization and enhance operational efficiency within the practice. You manage the commercial success of your squads, ensuring data delivery models (from migration to modern data stack implementation) are executed profitably, scalably, and cost-effectively.
- Execution & Technical Resolution
- Technical Escalation: Serve as the primary escalation point for delivery issues, personally leading the resolution of complex data integration bottlenecks and pipeline failures to protect client timelines and data reliability standards.
- Quality Enforcement
- Quality Oversight: Execute and monitor technical data quality standards, ensuring engineering teams adhere to strict policies regarding data lineage, automated quality checks (observability), security/privacy compliance (GDPR/CCPA/PII), and active catalog management.
2. Strategic Growth & Practice Scaling
- Talent & Scaling Execution: Execute the strategy for data engineering talent acquisition and development within your business units. Implement objective metrics to assess and grow the 'Data-Native' DNA of your teams, ensuring squads are consistently equipped to handle petabyte-scale environments and high-impact delivery.
- Offerings Alignment: Drive the adoption of standardized regional offerings (e.g., Modern Data Platform, Data Mesh, Lakehouse Implementation). Ensure your teams leverage the profitable frameworks defined by the practice to accelerate time-to-insight and eliminate architectural fragmentation in client environments.
- Innovation & IP Development: Lead the practical integration of Vector Databases and LLM-ready architectures into project delivery. Champion the hands-on development of IP and reusable accelerators (e.g., automated ingestion engines) that improve delivery speed and enhance data availability across your portfolio.
3. Leadership & Unit Management
- Unit Leadership: Directly lead, mentor, and manage the Engineering Managers and Lead Architects within your business unit. Hold your teams accountable for project-level operational consistency, technical talent development, and strict adherence to the practice's data governance standards.
- Stakeholder Communication: Clearly articulate the business unit’s operational performance, technical quality metrics, and delivery progress to the C-suite Stakeholders and regional client leadership, bridging the gap between technical execution and business value.
- Ecosystem Alignment: Maintain strong technical relationships with key partner contacts (Snowflake, Databricks, AWS/GCP). Align team delivery capabilities with current product roadmaps and ensure squad-level participation in training, certifications, and partner-led enablement opportunities.
Welcome to Searce
The ‘process-first’, AI-native modern tech consultancy that's rewriting the rules.
We don’t do traditional.
As an engineering-led consultancy, we are dedicated to relentlessly improving the real business outcomes. Our solvers co-innovate with clients to futurify operations and make processes smarter, faster & better.
Functional Skills
1. Delivery Management & Operational Excellence
- Methodology Execution: Expert capability in implementing and enforcing a unified delivery methodology (DataOps, Agile, Mesh Principles) within specific business units. Proven track record of auditing squad-level adherence to ensure consistency across the project lifecycle.
- Operational Performance: High proficiency in managing day-to-day operational metrics, including squad utilization, resource forecasting, and productivity tracking. Skilled at optimizing team performance to meet profitability and efficiency targets.
- SOW & Risk Mitigation: Proven experience in operationalizing Statement of Work (SOW) requirements and identifying technical delivery risks early. Expert at mitigating scope creep and data-specific bottlenecks (e.g., latency, ingestion gaps) before they impact client outcomes.
- Technical Escalation Leadership: Demonstrated ability to lead "war room" efforts to resolve complex pipeline failures or data integrity issues. Skilled at providing clear, rapid remediation plans and communicating technical status directly to regional stakeholders.
2. Architectural Implementation & Technical Oversight
- Modern Stack Proficiency: Deep, hands-on expertise in implementing Cloud-Native architectures (Lakehouse, Data Mesh, MPP) on Snowflake, Databricks, or hyperscalers. Ability to conduct deep-dive architectural reviews and course-correct design decisions at the squad level to ensure scalability.
- Operationalizing Governance: Proven experience in embedding data quality and observability (completeness, freshness, accuracy) directly into the CI/CD pipeline. Responsible for technical enforcement of regulatory compliance (GDPR/PII) and maintaining the integrity of data catalogs across active projects.
- Applied Domain Expertise: Practical experience leading the delivery of high-growth solutions, specifically Generative AI infrastructure (RAG, Vector DBs), Real-Time Streaming, and large-scale platform migrations with a focus on zero-downtime execution.
- DataOps & Engineering Standards: Expert-level mastery of DataOps, including the setup and management of orchestration frameworks (Airflow, Dagster) and Infrastructure as Code (IaC). You ensure that automation is a baseline requirement, not an afterthought, for all delivery teams.
3. Unit Management & Commercial Execution
- Unit & Team Management: Proven success in leading and mentoring Engineering Managers and Lead Architects. Responsible for the operational metrics, technical output, and career development of the business unit's talent pool.
- Offerings Implementation & Scoping: Expertise in translating service offerings (e.g., Data Maturity Assessments, Lakehouse Builds) into accurate project scopes, technical estimates, and resource plans to ensure delivery is both profitable and competitive.
- Talent Growth & Mentorship: Functional ability to implement growth frameworks for data engineering roles. Focus on hands-on coaching and scaling high-performance technical talent to meet the demands of complex, petabyte-scale environments.
- Partner Enablement: Functional competence in managing regional technical relationships with major partners (Snowflake, Databricks, GCP/AWS). Drives squad-level certifications, joint technical enablement, and alignment with partner product roadmaps.
Tech Superpowers
- Modern Data Architect – Reimagines business with the Modern Data Stack (MDS) to deliver data mesh implementations, insights, & real value to clients.
- End-to-End Ecosystem Thinker – Builds modular, reusable data products across ingestion, transformation (ETL/ELT), governance, and consumption layers.
- Distributed Compute Savant – Crafts resilient, high-throughput architectures that survive petabyte-scale volume and data skew without breaking the bank.
- Governance & Integrity Guardian – Embeds data quality, complete lineage, and privacy-by-design (GDPR/PII) into every table, view, and pipeline.
- AI-Ready Orchestrator – Engineers pipelines that bridge structured data with Unstructured/Vector stores, powering RAG models and Generative AI workflows.
- Product-Minded Strategist – Balances architectural purity with time-to-insight; treats every dataset as a measurable "Data Product" with clear ROI.
- Pragmatic Stack Curator – Chooses the simplest tools that compound reliability; fluent in SQL, Python, Spark, dbt, and Cloud Warehouses.
- Builder @ Heart – Writes, reviews, and optimizes queries daily; proves architectures with cost-performance benchmarks, not slideware. Business-first, data-second, outcome focused technology leader.
Experience & Relevance
- Executive Experience: Minimum 10+ years of progressive experience in data engineering and analytics, with at least 3 years in a Senior Manager or Director -level role managing multiple technical teams and owning significant operational and efficiency metrics for a large data service line.
- Delivery Standardization: Demonstrated success in defining and implementing globally consistent, repeatable delivery methodologies (DataOps/Agile Data Warehousing) across diverse teams.
- Architectural Depth: Must retain deep, current expertise in Modern Data Stack architectures (Lakehouse, MPP, Mesh) and maintain the ability to personally validate high-level architectural and data pipeline design decisions.
- Operational Leadership: Proven expertise in managing and scaling large professional services organizations, demonstrated ability to optimize utilization, resource allocation, and operational expense.
- Domain Expertise: Strong background in Enterprise Data Platforms, Applied AI/ML, Generative AI integration, or large-scale Cloud Data Migration.
- Communication: Exceptional executive-level presentation and negotiation skills, particularly in communicating complex operational, data quality, and governance metrics to C-level stakeholders.
Join the ‘real solvers’
ready to futurify?
If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘Art of the possible’. Don’t Just Send a Resume. Send a Statement.

About Searce Inc
About
What is ‘searce’
Searce means ‘a fine sieve’ & indicates ‘to refine, to analyze, to improve’. It signifies our way of working: To improve to the finest degree of excellence, ‘solving for better’ every time. Searcians are passionate improvers & solvers who love to question the status quo.
The primary purpose of all of us, at Searce, is driving intelligent, impactful & futuristic business outcomes using new-age technology. This purpose is driven passionately by HAPPIER people who aim to become better, everyday.
What we do
Searce is a modern tech consulting firm that empowers clients to futurify their businesses, leveraging Cloud, AI & Analytics.
- We are a category defining niche’ cloud-native technology consulting company, specializing in modernizing (improve, automate & transform) the full-scope of infra, app, process & work
- We partner with clients in their ‘beyond x’ journey to drive intelligent, impactful & futuristic business outcomes
- We are the most preferred tech partner of choice when it comes to ‘solving for better’ for the new-age tech startups & digital enterprises, leading disruption in their industries
- Our Service Offerings: We offer Advanced Cloud, Data & App Modernization, Cloud Consulting, Management & Improvement (DevOps, SysOps & Cloud Managed Services), Applied AI & Analytics services
- As one of the top 5 niche’ full scope global partners for Google Cloud & a preferred partner for AWS, we are the most preferred ‘engineering-led’ tech company of choice when it comes to solving complex business problems.
Who we are
We are passionate improvers, solvers & futurists. Driven by our engineering excellence mindset, we care most about delivering intelligent, impactful & futuristic business outcomes. Searcians are motivated by continuous improvement & solving for better in everything we do.
At the core, a Searcian is self-driven to become better, everyday. In passionate pursuit of the finest degree of excellence we drive exceptional outcomes in everything we do.
We believe that trust is the most important value. We also believe that we need to ‘earn the trust’, everytime one engages with us. And earning trust for us is far more important than anything else. We aim to be the *most trusted* tech consulting partner for our clients.
We are HAPPIER at heart. Humble, Adaptable, Positive, Passionate, Innovative, Excellence focused, & Responsible. We live the HAPPIER Culture Code.
Being HAPPIER.
How we work
- Customers. Partners. Our aim is to build relationships with customers for life. And meaningfully improve the life of every customer.
- We do what we say. We say what we do. We are uncomfortably honest and transparent. Being genuine wins trust & makes people happier.
- Mistakes are encouraged. We make mistakes. Tons of those. Everyday. And we don’t mind apologizing to our juniors, peers or superiors. We are no ego-doers.
- Underpromise. Overdeliver. We work with a deep desire to go above and beyond in everything we do. Everytime.
So, If you are passionate about tech, future & what you read above (we really are!), apply here to experience the ‘Art of Possible’
Connect with the team
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Bangalore/Hyderabad
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Description
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The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.
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- Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
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- Work with large datasets to ensure data quality, consistency, reliability, and performance.
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Requirements
Candidates who demonstrate:
- 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
- Strong hands-on experience with Apache Spark and Scala.
- Experience designing, building, and maintaining large-scale ETL pipelines.
- Strong hands-on experience with AWS, particularly Amazon S3.
- Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
- Strong SQL skills and a solid understanding of distributed data processing concepts.
- Experience working with batch and/or streaming data pipelines.
- Excellent debugging, problem-solving, and performance optimization skills.
- Strong communication and collaboration skills.
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- Experience with Databricks and the broader Databricks data platform.
- Familiarity with streaming technologies such as Apache Kafka.
- Experience working on large-scale data platforms handling high-volume data workloads.
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Job Summary
We are looking for an experienced Data Engineer to build and optimize scalable data pipelines and AWS-based data platforms.
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- Develop and maintain data pipelines using Python and PySpark.
- Design and implement data lakehouse solutions on AWS.
- Build and manage data workflows using AWS Glue, EMR, ECS, and Step Functions.
- Work with DynamoDB and other data storage technologies.
- Monitor data pipelines and ensure data quality, performance, and reliability.
- Collaborate with stakeholders and engineering teams to deliver data solutions.
Mandatory Skills
- 5+ years of overall professional experience.
- 3+ years of experience in data platforms and big data technologies.
- Strong proficiency in Python and PySpark.
- Hands-on experience with AWS EMR, ECS, Glue, Step Functions, and DynamoDB.
- Experience designing and implementing AWS data lakehouse architectures.
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Job Description – Senior GCP Data Engineer
Position: Senior GCP Data Engineer
Experience: 8+ Years
Budget: Up to 30 LPA
Location: Bangalore / Hyderabad
Relocation: Candidates from Chennai / Pune willing to relocate are also welcome.
Notice Period: Immediate to 15 Days
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- Round 1: Virtual Interview
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- Strong hands-on experience in GCP Data Engineering.
- Good programming experience in Python.
- Strong experience with PySpark / Apache Spark.
- Proficiency in SQL and database concepts.
- Experience in developing and maintaining data pipelines.
- Good understanding of data processing, transformation, and integration.
- Experience in handling large-scale data processing.
- Strong problem-solving and debugging skills.
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- Experience in optimizing data pipelines for performance and scalability.
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- Previous experience in a Lead Data Engineer role.
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- Ability to collaborate with cross-functional teams and deliver data engineering solutions.
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- Design, develop, and maintain scalable data pipelines using GCP.
- Develop data processing and transformation workflows using Python and PySpark.
- Write and optimize SQL queries for efficient data processing.
- Build and maintain reliable data engineering solutions.
- Troubleshoot data pipeline issues and optimize performance.
- Ensure data quality, reliability, and scalability.
- Collaborate with stakeholders and team members to deliver project requirements.
- Lead and guide team members where applicable.
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Job Details :
- Number of Positions : 3
- Employment Type : Contract-to-Hire (C2H)
- Experience Required : 5 to 8 Years
- Location : Manyata Tech Park, Bengaluru
- Work Mode : Hybrid – 3 Days in Office per Week
- Preferred Notice Period : Immediate to 15–20 Days
- Project Duration : Initially 6–8 months, with a strong possibility of extension for long-term projects
- Relocation : Candidates from outside Bengaluru can apply if they are willing to relocate.
Role Overview :
We are looking for experienced Senior Data Engineers with strong expertise in Google Cloud Platform (GCP), Apache Spark, BigQuery, and SQL to design, develop, and maintain scalable data pipelines and distributed data processing solutions.
The ideal candidate should have hands-on experience with cloud-based data engineering, end-to-end pipeline architecture, system design, performance optimization, debugging, and CI/CD practices.
Mandatory skills :
Apache Spark (PySpark / Scala), SQL, GCP, BigQuery, GCP Dataproc, Data Pipelines, System Design, CI/CD, Debugging & Problem-Solving.
Key Responsibilities :
- Design, develop, and maintain scalable data pipelines using Apache Spark, PySpark, and/or Scala.
- Build and optimize batch and distributed data processing workflows on GCP.
- Work extensively with BigQuery for data transformation, validation, analytics, and query optimization.
- Develop and maintain data workflows using GCP services, including Dataproc, BigQuery, and Cloud Storage.
- Design end-to-end data pipeline architectures with a focus on scalability, reliability, and performance.
- Write and optimize complex SQL queries for large-scale datasets.
- Troubleshoot and resolve data processing issues, pipeline failures, and performance bottlenecks.
- Implement CI/CD practices for data engineering workflows, testing, and deployments.
- Monitor pipeline performance and identify opportunities for continuous improvement.
- Collaborate with cross-functional teams to understand requirements and deliver reliable data solutions.
- Participate in code reviews, testing, production deployments, and production support.
Mandatory Skills :
- Strong hands-on experience with Apache Spark, including PySpark and/or Scala.
- Strong SQL skills and experience working with large-scale datasets.
- Hands-on experience with Google Cloud Platform (GCP).
- Practical experience with BigQuery and data warehousing concepts.
- Working knowledge of GCP Dataproc.
- Strong understanding of data pipelines, end-to-end workflows, and system design.
- Practical experience with CI/CD and deployment processes.
- Excellent debugging, troubleshooting, and problem-solving skills.
Preferred Skills :
- Experience with Apache Airflow, Google Cloud Composer, or similar orchestration tools.
- Knowledge of Kafka or other streaming and messaging technologies.
- Proficiency in Python for data engineering and automation.
- Experience with Git-based development workflows.
- Knowledge of Linux/Unix environments and shell scripting.
- Familiarity with monitoring, logging, data quality, and governance practices.
Required Qualifications :
- 5 to 8 years of experience in Data Engineering, Big Data Engineering, or a related field.
- Proven experience developing and maintaining scalable data pipelines.
- Strong understanding of distributed data processing and cloud-based data architecture.
- Ability to troubleshoot complex technical issues and optimize pipeline performance.
- Strong communication, collaboration, and ownership skills.
Interview Process :
Round 1 : AI-Based Interview
- Candidates must complete the AI interview as soon as possible after receiving the invitation.
Round 2 : Virtual Interview Drive
- Interviews are planned for the upcoming drive; exact dates will be communicated separately.
Round 3 : Online Client Interview
- Shortlisted candidates will attend the online interview.
- Candidates must be available throughout the day on the scheduled interview date.










